NBSIR 77-1276

FILE COPY

BO NOT REMO

OMNITAB II User's Reference

Manual 1977 Supplement

David Hogben

Sally T. Peavy

Institute for Basic Standards

National Bureau of Standards

Washington, D.C. 20234

July 1977

Final

Prepared for National Highway Traffic Safety Administration

Department of Transportation

Washington, D.C. 20590

NBSIR 77-1276

OMNITAB II USER'S REFERENCE

MANUAL 1977 SUPPLEMENT

David Hogben

Sally T. Peavy

Institute for Basic Standards

National Bureau of Standards

Washington, D.C. 20234

July 1977

Final

Prepared for National Highway Traffic Safety Administration

Department of Transportation

Washington, D.C. 20590

U S. DEPARTMENT OF COMMERCE, Juanita M. Kreps, Secretary

Dr. Sidney Harman. Under Secretary

Jordan J. Baruch, Assistant Secretary for Science and Technology

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Acting Director

OMNITAB II User's Reference Manual

1977 SUPPLEMENT

by

David Hogben and Sally T. Peavy

Preface

In the early 1960's Joseph Hilsenrath designed a totally new general purpose numerical

scientific and statistical language known as OMNITAB. The heart of the OMNITAB design was

the universal worksheet concept and the use of very simple English like instructions to

perform scientific calculations on data in columns of the worksheet. With the help of

numerous colleagues, the language was developed to the point where it became a very

effective and easy to use tool for the analysis of data.

The first full documentation appeared in NBS Handbook 101. NBS Handbook 101 was published in 1966 and reissued in 1968. The original OMNITAB was designed for use with IBM

7094 computers.

At first, OMNITAB was distributed informally. In 1966 work began to redesign OMNITAB to

make it as machine independent as possible and to improve it as much as possible. The

responsibility for this development was first assumed by Walter Gilbert of the Computer

Services Division. Two years later the responsibility for the development and maintenance

of the OMNITAB computing system was assumed by David Hogben and Sally T. Peavy,

respectively, of the Statistical Engineering Laboratory.

In 1969 development of QMNITAB stopped temporarily to allow time to thoroughly test and

document the system. The result was the release of the "OMNITAB II Magnetic Tape and

Documentation Parcel" to the National Technical Information Service for official

distribution in November 1970. Less than one year later, the documentation, consisting of

four publications, was completed. The version distributed by NTIS is known as OMNITAB II Version 5.0.

After 1971, development of QMNITAB resumed. Errata have been continuously sent to

recipients of the "OMNITAB II Magnetic Tape and Documentation Parcel." However,

improvements and additions have not been released.

In 1977, a new version of CMNITAB should be released to NTIS, which will incorporate all

the many improvements made since 1971. The new version will be called CMNITAB 1977. (It

will be known within the National Bureau of Standards as CMNITAB II Version 6.0) In 1970

OMNITAB became known as CMNITAB II to emphasize the machine independent feature of the

computing system. For simplicity the Roman numeral II is now being replaced by the more

informative 1977.

Completely new documentation will be prepared for the CMNITAB 1977 general-purpose

statistical, numerical and data analysis computing system. This report is an interim

document to. supplement "CMNITAB II User's Reference Manual," NBS Technical Note 552. The

supplement will describe all improvements and additions which have been made since 1971. No

attempt is made here to correct the numerous typographical errors in NBS Technical Note 552.

That will be done later when this supplement is merged with NBS Technical Note 552 in a new

1
OMNITAB reference manual. It is assumed that readers of this supplement are thoroughly

familiar with the "OMNITAB II User's Reference Manual."

An effort was made to make OMNITAB II as machine independent as possible with great success within the state of the art in 1970. OMNITAB 1977 will be much more portable and

essentially the only machine dependent features will be machine constants and the size of

the system, which limits the implementation of QMNITAB to large computers (the equivalent of

approximately 64K 36 -bit words of memory with overlay).

OMNITAB II has been used primarily with batch processing. QMNITAB 1977 will be fully

interactive with either wide or narrow input devices.

Notation

The structure of this supplement closely parallels the structure of the "OMNITAB II User's Reference Manual," NBS Technical Note 552. PART A of NBS TN 552 is not modified.

The supplement contains PART B SUPPLEMENT, PART C SUPPLEMENT and PART D SUPPLEMENT for PART

B, PART C and PART D of NBS TN 552. A table of contents is given for the entire Supplement.

The numbering of sections will follow the style of NBS TN 552 with the following

modifications.

(1) A revision of a section will have an asterisk (*) after the section number, as in

4.2* Analysis Of One Column Of Data .

(2) An addition to a section will have a plus (+) after the section number, as in

3.3+ Manipulative Operations .

(3) A new section will have a period and a numeral after the number of the section

which it is supposed to follow. For example, section

1.5.1 Extended Plotting Capability ,

is to be inserted after

1.5 Plotting Data

and before

1.6 Optional Forms Of Readable Printing .

If more than one section is to be inserted, then the sections will be numbered

consecutively as 1.5.1, 1.5.2, etc.

Acknowledgments

OMNITAB is a very large system whose great success is the result of efforts by many

people. First, there was the original design by Joseph Hilsenrath. Then came the work of

the other authors of NBS Handbook 101 and the many people who contributed ideas and

programming effort for that book. Next came the work of Walter Gilbert, Ruth N. Varner and

many others who contributed to the development of OMNITAB II Version 5.0.

In addition to above persons , QMNITAB 1977 has been developed with the assistance of

Shirley G. Bremer. Every other member of the staff of the Statistical Engineering

Laboratory has contributed valuable suggestions for improvements and editorial assistance.

2
James J. Filliben made major contributions to the documentation and development of the

probability plotting instructions, the STATISTICAL analysis instruction, and the STATPLOTS

instruction. Roy H. Wampler provided a subprogram needed for the STATISTICAL analysis

instruction and gave assistance during the implementation of new algorithms for the MINVERT

and SOLVE instructions. Irene Stegun and Ruth Zucker contributed a program for evaluating

special integrals. G. M. Fumival, Yale University, and R.W. Wilson, USDA Forest Service,

provided a program used for the BESTCP instruction. Wesley L. Nicholson developed the

algorithms needed for the instructions used to produce stem and leaf displays. The

development of the editing instructions was stimulated by reading "Student Handbook for the

Minitab Statistical Computing System," Thomas A Ryan, Brian L. Joiner and Barbara F Ryan,

Pennsylvannia State University (1975)

.

OMNITAB is a mission-oriented computing system designed to meet scientific needs. Based

upon experience with scientific problems, many users, too numerous to mention, have made

invaluable suggestions for improvements, additions and corrections.

The National Highway Traffic Safety Administration, Department of Transportation,

provided financial support for accelerated development of OMNITAB 1977 ; particularly for the

development of instructions for table making and data editing. The Computer Services

Division, ICST, provided computing support for the entire development of OMNITAB 1977.

3
TABLE OF CONTENTS

Preface ................ 1 Notation ................ 2 Acknowledgments .............. 2 Table Of Contents .............. 4

PART B SUPPLEMENT : THE OMNITAB II COMPUTING SYSTEM 6

1. HOW TO USE OMNITAB II 7

1.4* NBS Operating System Control Cards ....... 7 1.6* NRMAX 7

3. DIAGNOSTIC FEATURES AND ACCURACY 8

3.2* Fatal Errors ............ 8 3.3* Arithmetic Faults ........... 9 3.4* Informative Diagnostics ......... 9 3.6* Accuracy Of Instructions ......... 11

4. FOR MORE EFFECTIVE USE OF OMNITAB II 12

4.6.1

Use Of FASTRAND 12

PART C SUPPLEMENT: DESCRIPTIONS OF INSTRUCTIONS 16

1. ENTERING AND PRINTING DATA 17

1.1.1 Use Of OMNITAB With Terminals 17 BRIEF, FULL, INTERACTIVE, LENGTH, LOCAL, REMOTE, TERMINAL, WIDTH

1.1.2 Use Of Labels . . . . . . . . . . .21

ALABEL, LABEL, MLABEL

1.1.3 Multilingual OMNITAB II 26

DANSK, DEUTSCH, ENGLISH, ESPANOL, FRANCAIS, ITALIANO, JAPANESE,

NEDERLANDS, NORSK, PORTUGESE, SLOVENE, SVENSKA, YUGOSLAV

VOCABULARY

1.3* Common Printing Instructions ........ 26

1.5.1 Extended Plotting Capability ........ 26

CPLOT, FOURPLOTS, NCPLOT, NPLOT, TWOPLOTS

1.5.2 Use Of Calcomp Plotter . ........ 35

CALCOMP AXIS, CALCOMP FAST, CALCOMP PAPER, CALCOMP PLOT,

CALCOMP SIZE, CALCOMP SLOW, CALCOMP SPEED, CALCOMP TAPE "L"

1.6* Optional Forms Of Readable Printing ....... 45

1.7* Formatted Printing And Reading ........ 45

1.10* Use Of Magnetic Tapes, Drums, Discs, etc. ...... 45

BACKSPACE UNIT "L", CREAD UNIT "L", CREAD UNIT "L" "L",

CSET UNIT "L", ENDFILE UNIT "L", READ UNIT "L", READ UNIT "L" "L",

REWIND UNIT "L", SET UNIT "L", SKIP UNIT "L", UNIT, TAPE,

WRITE UNIT "L", WRITE UNIT "L" "L"

2. ARITHMETIC OPERATIONS 49

2.6+ Data Summarization .......... 49

AVERAGE, DAYS, MEDIAN, PERCENTAGE, PROPORTION, RANGE, STDDEV

4
3. DATA MANIPULATION 52

3.3+ Manipulative Operations ......... 52

CENSOR EQ, CENSOR GE, CENSOR GT, CENSOR LE, CENSOR LT, CENSOR NE

3.5.1 Editing Data ............ 53

CHOOSE, CODE, DELETE, OMIT, RECODE, REPLACE, RETAIN

4. STATISTICAL ANALYSIS 60

4.1* Elementary Analysis .......... 60

4.1.1 Stem-and-Leaf Displays .......... 63

STEM LEAF, SSTEM LEAF

4.1.2 Statistical Plots ........... 72 STATPLOTS

4.1.3 Table Making ............ 78

NTABLE AVERAGE, NTABLE FREQUENCY, NTABLE MAXIMUM, NTABLE MEDIAN,

NTABLE MINIMUM, NTABLE PERCENTAGE, NTABLE PROPORTION, NTABLE RANGE,

NTABLE STDDEV, NTABLE SUM, TABLE AVERAGE, TABLE FREQUENCY,

TABLE MAXIMUM, TABLE MEDIAN, TABLE MINIMUM, TABLE PERCENTAGE,

TABLE PROPORTION, TABLE RANGE, TABLE STDDEV, TABLE SUM

4.2* Analysis Of One Column Of Data ........ 96

4.3* Analysis Of Groups of Data ......... 104

4.4* Analysis Of Two-way Table ......... 116

4.4.1 Twoway Fixed Effects Design With Replication ..... 116

4.5* Regression ............ 125

4.5.1 Selection Of Variables In Linear Regression ..... 139

BESTCP

4.6+ Correlation ............ 144

4.7.1 Probability Plotting .......... 144

CAUCHY PLOT, DEXPONENTIAL PLOT, EXPONENTIAL PLOT,

EXTREME PLOT, GAMMA PLOT, HALFNORMAL PLOT, LAMBDA PLOT,

LOGISTIC PLOT, LOGNORMAL PLOT, NORMAL PLOT, PARETO PLOT,

POISSON PLOT, UNIFORM PLOT, WEIBULL PLOT

4.8+ References For Section 4 ........ 153

5. NUMERICAL ANALYSIS 154

5.1* Special Integrals ........... 154

COSINTEGRAL, EEXPINTEGRAL, EINTEGRAL, EXPINTEGRAL,

HCOSINTEGRAL , HSININTEGRAL, NEGEINTEGRAL, SININTEGRAL

5.1.1 Differences ............ 158

DIFFERENCES, DTVDIFFERENCES , SDIFFERENCES , SDIVDI FFF.RENCES

7. ARRAY OPERATIONS 161

7.4* Properties Of An Array .......... 161

8. MATRIX OPERATIONS 162

8.5* Matrix Analysis ........... 162

8.6* Matrix Properties ........... 163

11. + INDEX OF COMMANDS DESCRIBED IN PART C SUPPLEMENT 166

PART D SUPPLEMENT: LIST OF INSTRUCTIONS (arranged alphabetically) . . . .167

5
PART B SUPPLEMENT

THE CMNITAB II COMPUTING SYSTEM

6
1 . HOW TO USE OMNITAB II

1.4* NBS Operating System Control Cards .

Replace the first two control cards listed on page 16 of NBS TN 552 by:

@RUN , P/N NAMEXX , TASKNO-NAMEXX , PROJECTID ,MT ,MP/MC

0NBS*OMNITAB.

where

P = a single character F, J, M or N to denote the priority.

NAMEXX = the six character name assigned to you by the Computer Services Division.

TASKNO = the five digit task number assigned to you by the Computer Services Division.

PROJECTID = from 1 to 12 characters from the alphanumeric set A-Z and 0-9 and - and $.

The character $ is not recommended.

MT = maximum run tine; usually 1 for one minute. If the integer is preceded by S, the

maximum time is in seconds.

MP = maximum number of pages.

/MC = maximum number of cards to be punched. Optional, use only if cards are to be

punched

.

1.6* NRMAX .

The READ, SET and GENERATE (and all TAPE input) instructions have been modified to reset

NRMAX to the number of values put in the worksheet, regardless of whether the new value is

larger or smaller than the previous value

.

The set of instructions

SET data in column 1

7, 8, 3, 6, 4 SET data in column 2 14, 11, 17

formerly would have left NRMAX equal to 5, but in now NRMAX is reset to 3 and the following

would appear in the LIST OF DATA, INSTRUCTIONS AND DIAGNOSTICS

SET DATA IN COLUMN 1 7, 8, 3, 6, 4

* INFORMATIVE DIAGNOSTIC FOR THE ABOVE INSTRUCTION - NRMAX HAS BEEN RESET FROM 0 TO 5

.

SET DATA IN COLUMN 2 14, 11, 17

* INFORMATIVE DIAGNOSTIC FOR THE ABOVE INSTRUCTION - NRMAX HAS BEEN RESET FROM 5 TO 3

.

7
3 . DIAGNOSTIC FEATURES AND ACCURACY

3.2* Fatal Errors.

Many changes have been made to the fatal error messages. Some messages have been added.

Some changes were made to make the messages grammatically correct , some were made to make

them technically correct, and some were made to make them clearer. A complete list of all

the fatal error messages is printed below.

COMMAND DOES NOT EXIST.

INCORRECT STATEMENT NUMBER.

INCORRECT ARGUMENT IN INSTRUCTION.

INSTRUCTION IS NOT ALLOWED IN REPEAT MODE.

INSTRUCTIONS BETWEEN BEGIN AND FINISH CANNOT BE NUMBERED.

NRMAX = 0.

(n) IS AN INCORRECT NUMBER OF ARGUMENTS.

COLUMN NUMBER (S) OUTSIDE (n) COLUMN WORKSHEET.

TOO MANY NUMBERED INSTRUCTIONS.

INSTRUCTION NUMBER NOT FOUND.

DIMENSIONED WORKSHEET EXCEEDS LIMIT OF 12500.

ROW NUMBER (S) OUTSIDE (n) ROW WORKSHEET.

ARRAY OR MATRIX OUTSIDE (m) ROW X (n) COLUMN WORKSHEET.

INTEGER ARGUMENT IS LESS THAN -8191.

NUMBERED PERFORM INSTRUCTION EXECUTES ITSELF.

IMPROPER TYPE OF ARGUMENT.

INSTRUCTION MUST BE STORED.

MATRIX IS (NEARLY) SINGULAR.

INSUFFICIENT SCRATCH AREA.

DEGREE IS GREATER THAN NUMBER OF NON- ZERO WEIGHTS.

NEGATIVE WEIGHTS MAY NOT BE USED.

INCONSISTENT ROW AND COLUMN NUMBERS.

MISSING OR INCORRECT FORMAT OR QUALIFIER.

NUMBER OF ARGUMENTS SHOULD BE (n)

.

AN INCREMENT INSTRUCTION CANNOT INCREMENT ITSELF.

MATRIX IS NOT SYMMETRIC

.

STORAGE COLUMN NUMBERS CANNOT EQUAL OTHER COLUMN NUMBERS.

NRMAX IS LESS THAN 5 OR ALL MEASUREMENTS ARE THE SAME.

VALUE OF SOME MEASUREMENT IS NOT ACCEPTABLE.

I , J,K AND/OR L ARE NOT DEFINED CORRECTLY, OR RULE

CANNOT DETERMINE PROPER PARAMETER VALUES FOR THESE DATA.

THE ARGUMENTS *NRMAX* , **NRMAX**, *V*, OR **V**

CAN ONLY BE INCREMENTED 4 BY 0.0.

THE PROBABILITY DISTRIBUTION PARAMETER IS NOT ALLOWED.

LABEL OF A COLUMN (ARRAY, MATRIX) CANNOT BE A NUMBER.

A LABEL MUST HAVE AT LEAST 1 AND LESS THAN 13 CHARACTERS.

COLUMN (ARRAY, MATRIX) HAS BEEN PREVIOUSLY LABELED.

NUMBER OF LABELS EXCEEDS (n) COLUMN'S IN THE WORKSHEET.

LABEL IN ALABEL OR MLABEL MUST BE FOLLOWED BY 4 NUMBERS.

8
3.3* Arithmetic Faults.

Many changes have been made to the arithmetic messages. Some changes were made to make

the messages grammatically correct, some were made to make them technically correct, some were made to improve appearance, and some were made to reduce the amount of computer storage

required. A complete list of the new set of arithmetic fault messages is given below.

SQRT, LOG OR RAISE OF NEGATIVE NUMBER.

EXPONENT TOO SMALL OR TOO LARGE.

VALUE OUT OR RANGE AND INVERSE FUNCTION CANNOT BE EVALUATED.

X TOO LARGE FOR SIN(X) OR COS(X).

VALUE SCALED TO AVOID OVERFLOW OR UNDERFLOW.

DIVISION BY ZERO.

TRIGONOMETRIC FUNCTION NOT DEFINED.

ONE OF THE VALUES COMPARED IS ZERO. ABSOLUTE TOLERANCE USED.

X FOR ELLIPTICAL INTEGRAL IS GREATER THAN OR EQUAL TO ONE.

OVERFLOW FROM USE OF THE SUM ALGORITHM.

FUNCTION NOT DEFINED FOR NEGATIVE OR ZERO VALUES.

FUNCTION NOT DEFINED FOR ZERO VALUES.

3.4* Informative Diagnostics.

Many changes have been made to the informative diagnostic messages. Some messages have

been added. Some changes were made to make the messages grammatically correct, some were

made to make them technically correct, and some were made to make them clearer. A complete

list of all the informative diagnostic messages is given below.

TOO MUCH DATA AFTER SET, READ OR GENERATE.

ALL DATA WERE LOST AFTER ROW (n)

THE INSTRUCTION WAS EXECUTED, BUT CANNOT BE STORED.

VALUE REQUESTED WAS NOT FOUND.

COLUMN NUMBER INCORRECT OR NOT FOUND.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

ITS MEANING IS NOT CLEAR.

A VALUE OF F LESS THAN ZERO WAS SET EQUAL TO ZERO.

A VALUE OF DEGREES OF FREEDOM LESS THAN 1 WAS RESET TO 1. A VALUE OF DEGREES OF FREEDOM WAS TRUNCATED TO AN INTEGER.

TITLE NUMBER MUST BE 1, 2, 3 OR 4 and 1 WAS USED.

NO. ROWS NOT = TO NO. COLS. LARGEST SQUARE MATRIX WAS USED.

AN INCORRECT ASTERISK STRING IMPLYING THROUGH WAS IGNORED.

UNNECESSARY ARGUMENTS IN INSTRUCTION WERE IGNORED.

MATRIX EXTENDS BEYOND (n) ROW BY (n) COLUMN WORKSHEET.

ONLY PART OF THE MATRIX IS STORED IN THE WORKSHEET.

INSUFFICIENT SCRATCH AREA.

(n) = NRMAX IS NOT LARGE ENOUGH TO ALLOW ITERATION.

1ST COLUMN OF ISETUP OR ISOLATE IS NOT MONOTONIC

OR IS CONSTANT

.

ITERATION DID NOT FIND ANY ROOTS.

(n) ROW WORKSHEET IS TOO SHORT TO ACCOMMODATE ALL THE VALUES

GENERATED BY THIS INSTRUCTION.

NO EXTREMA WERE FOUND.

A TRIAD OF X'S WITH AT LEAST TWO IDENTICAL VALUES,

WAS FOUND AND IGNORED.

ONLY THE FIRST ARGUMENT IN THE INSTRUCTION WAS USED.

FORMAT WAS NOT FOUND. READABLE FORMAT WAS USED.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

ONE, SOME OR ALL WEIGHTS ARE NEGATIVE.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

ALL WEIGHTS ARE ZERO.

9
THE INSTRUCTION WAS IGNORED BECAUSE . .

.

VALUE OF FUNCTION IS TOO LARGE OR TOO SMALL.

COLUMN NOT LONG ENOUGH TO STORE ALL NUMBERS.

FIRST (n) NUMBERS WERE STORED.

NOT ENOUGH DATA IN COLUMN TO RESTORE MATRIX/ARRAY.

DATA AVAILABLE WAS USED.

SUMS OF SQUARES DO NOT ADD UP. ABSOLUTE VALUE

OF (TOTAL-ROW-COL-RESIDUAL) /TOTAL EXCEEDS 5.E-7.

MORE THAN 50 HEAD COLUMN INSTRUCTIONS AND/OR LABELS

HAVE BEEN USED. ONLY THE LAST 50 HAVE BEEN RETAINED.

ATTEMPT TO PROMOTE FROM BELOW NRMAX = (n)

FIRST ARGUMENT IS RESET TO = (n)

ATTEMPT TO DEMOTE BELOW THE (n) ROW WORKSHEET.

DATA BELOW ROW (n) IS LOST.

X FOR ELLIPTICAL INTEGRALS IS 1.0 OR GREATER.

TIE RESULT IS SET EQUAL TO 0.0.

NEGATIVE VALUE (S) WERE ENCOUNTERED BY PARTITION FUNCTION.

ZEROES STORED.

POSITIVE, INSTEAD OF NEGATIVE, TEMPERATURES USED.

CAUTION, USE EXPERIMENTALLY ONLY. NOT OPTIMUM IN ORDER TO

MAKE IT MACHINE INDEPENDENT. REFERENCES - J.B. KRUSKAL,

COMM. ACM, 12, 92. AND J.H. HALTON, SIAM REV., 12,1.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

COMMAND BEGINS WITH S AND STORAGE MUST BE REQUESTED.

NUMBER OF SIGNIFICANT DIGITS AFTER DECIMAL POINT,

HAS BEEN SET TO (n)

.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

ALL POINTS ARE OUTSIDE SPECIFIED LIMITS.

MATRIX OF PARTIAL CORRELATION COEFFICIENTS IS NOT SYMMETRIC

,

INDICATING POSSIBLE COMPUTING ERROR.

PARTIAL CORRELATION COEFFICIENTS ARE NOT DEFINED.

(n) MEASUREMENTS MUST EXCEED (n) VARIABLES.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

LOWER LIMIT OF AXIS EQUALS UPPER LIMIT.

PRINTING OF STEM AND LEAF DISPLAY IS NOT POSSIBLE.

INSTRUCTION WAS TREATED AS SSTEM LEAF.

(n) ROWS IN WORKSHEET IS NOT ENOUGH FOR COMPLETE STORAGE.

DISPLAY IS (n) LINES, ONLY FIRST 99 DISPLAYED.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

WIDTH = (n) IS TOO SMALL OR TOO LARGE.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

WIDTH = (n) IS TOO SMALL FOR A PLOT.

WIDTH = (n) IS INSUFFICIENT FOR PAGE PLOT. BEST PLOT GIVEN.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

ALL NUMBERS IN THE COLUMN ARE THE SAME.

COMPUTING PROBLEMS ENCOUNTERED. RESULTS MAY BE MEANINGLESS.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

COLUMN WAS HEADED BY LABEL INSTRUCTION.

NRMAX HAS BEEN RESET FROM (n) TO (n) . IMPLIED THROUGH FOR LABELS MUST BE IN ALPHABETICAL ORDER.

LENGTH = (n) IS TOO LARGE AND IS RESET FOR NORMAL USE.

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

LENGTH = (n) IS TOO SMALL FOR A PLOT.

ITERATION FAILED TO FIND AN EIGENVECTOR, (n) UNORDERED VALUES FOUND.

10
3.6+ Accuracy Of Instructions.

Accuracy is considered very important in the development of OMNITAB and much effort has

been spent to obtain very accurate results. A special new algorithm has been used for

summing numbers which is used for many instructions. Substantially better algorithms are now used for the MINVERT, MEIGEN and SOLVE instructions.

A much faster sorting procedure is now being used for the HIERARCHY, ORDER, SORT and

STATISTICAL analysis instructions.

11
4. FOR MORE EFFECTIVE USE OF CMNITAB II

4.6.1 Use of FASTRAND .

This section is for users of the NBS Computer only.

A set of CMNITAB II instructions and/or data may be stored on a mass storage device

(FASTRAND) for repeated use or editing. To reserve space on FASTRAND a file must be

catalogued. This is done once only by inserting the following control statement after the

RUN statement:

SASG,UP QUALIF IER*FILE.

where QUALIFIER and FILE are words with 1 to 12 alphanumeric characters.

Since a charge is made for mass storage, when it is no longer needed, the file should be

decatalogued with the following statement:

SDELETE , C QUALIFIER*FILE.

Entering information into FASTRAND .

Data or OMNITAB II sets of instructions can be entered as elements into a specified

file. Element names consist of 1-12 alphanumeric characters.

(a) To enter a set of CMNITAB II instructions into a catalogued file, use the following:

SRUN. .

.

@ASG,A QUALIFIER*FILE. (catalogued file)

SELT , IL QUALIFIER*FILE.ELEMENT1, ,F

(OMNITAB II instructions)

SEND F

A listing of the OMNITAB II instructions is printed along with a line identification number

to the left of the instruction set.

(b) To enter a set of data into a catalogued file, use the following:

SRUN . .

.

@ASG,A QUALIF IER*FILE. (catalogued file)

SELT , IDL QUALIFIER*FILE.DATA1,,FLG

(Data cards)

SEND FLG

Data will be listed along with the line identification nunber to the left of the data.

Using elements on FASTRAND file .

(a) Set of QMNITAB II instructions . A FASTRAND file can be used when a set of instructions, e.g., for generating reports,

does not change from one run to another, but the data do change. Construct an element (see

(a) above) consisting of all the instructions after the READ (or SET) instructions

12
(including a STOP instruction), but excluding the READ and CMNITAB instructions at the

beginning.

A set of instructions like the following can then be used:

@RUN

@ASG,A QUALIFIER*FILE.

@NBS*OMNITAB.

CMNITAB 9/29/76

READ data into columns i***io $ if that is where data is expected

(data)

@ADD QUALIFIER*FILE. ELEMENT1

@FIN

(b) Data as an element in FASTRAND file .

Let element DATA consist of data to be analyzed with a set of CMNITAB instructions. The

control cards necessary to use the element are as follows:

@RUN . .

.

@ASG,A QUALIFIER*FILE.

@NBS*OMNITAB.

CMNITAB 10/18/76

READ data into columns (C) , (C) , . .

.

(C)

@ADD QUAL IF IER*FILE. DATA

STOP

@FIN

The complete data element will be read into the worksheet and listed with the list of

instructions, data and diagnostics unless a NO LIST precedes the READ command.

Editing elements within a file .

Corrections , additions and/or deletions may be made to elements containing either data

or CMNITAB instructions. Whenever an element is created in a file and a listing is obtained, the successive lines (or cards) of the element are sequentially numbered to the

left of the listing. In altering an element, these numbers are used on a line correction

statement as follows

:

-n,m

The minus sign (-), which must be in column 1, is the correction indicator. No imbeded

spaces are allowed. All lines n through m in the element are to be replaced by the lines

(cards) immediately following until another correction indicator is read. If no lines of

correction follow the -n,m line correction statement, lines n through m are deleted.

The line correction statement -n (without a comma m) indicates that all statements after

it are to be inserted into the element after line n until another correction indicator or control statement is encountered.

If corrections are to be inserted before the first line in the element, the correction

lines are placed immediately after the control card

@ELT,UL QUALIFIER*FILE. ELEMENT,,

F

13
without specifying any insertion line number. The line correction statements irust be in

ascending order.

For example, suppose the element CODE is to be corrected as follows:

i. change line number 13 from STATIS of column 4

to

SSTATIS of column 4,

ii. delete the PRINT instructions with line numbers 23 and 24, and

iii. insert a PLOT instruction after line number 5.

The set of control statements will be as follows

:

@RUN

@ASG,A QUALIFIER*FILE.

@ELT,UL QUALIFIER*FILE.CODE,,F

-5 PLOT col 1 vs . col 2 -13, 13

SSTATIS col 4 -23, 24 @END F

The element CODE will be updated with the corrections and a new listing with new line

numbers printed. To alter an element consisting of data, the same procedure in the above

example may be used to correct data lines.

Saving Data For Future Use .

At times part or all of the data and results in the worksheet need to be saved for use

at some later date. This can be done easily by storing columns in an element on FASTRAND as follows

:

@ASG,A QUALIFIER*FILE.

@ASG,T TEMP.

@USE 7., TEMP.

@NBS*OMNITAB.

CMNITAB ... (set of instructions)

WRITE UNIT A columns (C) , (C) ,

. . .

,

(C)

BACKSPACE UNIT A 1 ENDFILE UNIT A

STOP

@COPY , I TEMP., QUALIFIER*FILE .ELEMENT

appropriate numbers should be substituted for the columns (C) , (C) , , (C) which have to

be saved. FILE denotes the name of a user's previously catalogued file and ELEMENT stands

for the element where the data is to be stored.

For future use, all that needs to be done is:

@ASG,A QUALIFIER*FILE.

CMNITAB

14
READ data into columns (C) , (C) ,

. .., (C) @ADD QUALIFIER*FILE. ELEMENT

STOP

General

The above description of the use of FASTRAND files is only applicable to the NBS UNIVAC

1108 computer under EXEC-8 control. It is not an attempt to be a complete discussion of

EXEC-8 control cards. The methods described herein may be implemented in many ways using

other EXEC-8 control cards. For further information check with the consultant in the

computer room or the references listed below.

References .

NBS Computer User's Guide , Technical Services Section, CSD, (1975), Section 4.

UNIVAC 1100 Operating System Programmer Reference , UP-4144 Rev. 3, Sperry Rand Corp.

,

sections 3.7, 8.2.7, 9.5, and IS . 2

.

15
PART C SUPPLEMENT

DESCRIPTIONS OF INSTRUCTIONS

16
1 . ENTERING AND PRINTING DATA

1.1.1 Use Of OMNITAB With Terminals .

BRIEF, FULL, INTERACTIVE, LENGTH, LOCAL, REMOTE, TERMINAL, WIDTH

The instructions described in this section were developed for interactive use of OMNITAB

from terminals. The instructions BRIEF, FULL, LENGTH and WIDTH may also be used for the

batch processing of instructions. All the instructions affect printing in one way or

another. The instructions LOCAL and REMOTE control the location of printing. The

instructions INTERACTIVE, LENGTH and WIDTH control the number of characters per line and the

number of lines per page. The instructions BRIEF, FULL and TERMINAL control the amount of

printing. The instructions LENGTH and WIDTH can be used very effectively to obtain plots of

different sizes with either batch or remote processing.

BRIEF $ no arguments

An S before some commands, as in SFIT, suppresses all the automatic printing of the

instruction. The instruction BRIEF suppresses only part of the automatic printing of all

instructions which follow it. At the present time, BRIEF has no arguments and only affects

the automatic printing of the STATISTICAL analysis, FIT and POLYFIT instructions.

BRIEF suppresses all pages after the first page of the automatic printing of the

STATISTICAL analysis instructions. For the FIT and POLYFIT instructions, it suppresses the

printing of the first page, the last two plots of the standardized residuals, the variancecovariance matrix, the second half of the analysis of variance and the fit omitting the last

column (term)

.

BRIEF is in effect for all subsequent instructions until it is countermanded by a FULL

instruction described below.

At some later time an argument may be added to the BRIEF instruction to indicate

particular parts of the automatic printing which are to be suppressed.

FULL $ no arguments

A FULL instruction can be used to countermand any and all BRIEF instructions which

precede it.

These two instructions make it possible to use OMNITAB II more effectively from a terminal

.

INTERACTIVE use from a terminal

When this instruction is used, instructions can be typed on a terminal. If an error

occurs, the error message will be printed immediately after the instruction is typed, in

addition to being printed in the LIST OF COMMANDS, DATA AND DIAGNOSTICS. The instruction

can then be corrected and retyped. All instructions following an instruction with a fatal error will be executed, which is not the case if an INTERACTIVE instruction has not been

used.

17
An INTERACTIVE instruction should appear at the beginning of a set of instructions and

should not be stored for repeated use.

The maximum number of characters which can be printed on a line is changed from 120 to

72. See the description of WIDTH below for amplification of this effect. The line width

can be modified by using the following optional form.

/ INTERACTIVE use with (c) characters per line /

/ /

This instruction is the same as the one above; except it also allows one to specify the

maximum number of characters printed on any one line. It has the same effect of using both

INTERACTIVE with no arguments and the WIDTH instruction described below. If OMNITAB is used

with a wide carriage terminal, the instruction:

INTERACTIVE 120

can be used to take full advantage of the wide carriage.

The argument (n) of an INTERACTIVE (n) instruction only refers to the number of

characters per line to be printed. It does not affect the number of characters per line

which are read. If the argument is greater than 80, only the first 80 characters (one card

image) will be used by any read instruction.

LENGTH equal to (n) lines printed per page

A LENGTH instruction controls the number of lines per page and the size of plots. It only affects the printing of PRINT, PLOT, PAGE PLOT, NPLOT, CPLOT and NCPLOT. (NPLOT, CPLOT

and NCPLOT are new instructions described in Section Cl. 5.1.) Its primarily use is for

changing the size of plots.

For any PRINT instruction, the maximum number of printed lines per page is normally 50.

The number 50 does not include two lines available for the printing of the OMNITAB

instruction, two lines of titles, a one -line NOTE at the bottom of the page, or blank lines

between groups of numbers. The argument of a LENGTH instruction can be set to a number

smaller than 50 for short pages (with batch use) or to a larger number for interactive use.

Normally, a plot has 50 intervals, or 51 divisions (lines), on the vertical axis.

Smaller or larger plots can be obtained by changing the value of LENGTH. The number of

intervals on the vertical axis is always some multiple of ten as indicated by the formula:

number of intervals = 10 times the integral part of [(n-5)/10].

18
The following table gives a value of the argument (n) of LENGTH for different plot sizes:

Intervals LENGTH (n)

10 15-24

20 25-34

30 35-44

40 45-54

50 55-65

60* 65-74

70* 75-84

80* 85-94

90* 95-104

100* 105-114

150* 155-164

200* 205-214

300* 305-314

400* 405-414

500* 505-514

* These sizes are only available if an INTERACTIVE instruction has been used nreviously.

A LENGTH instruction can be used very effectively to produce plots of different sizes.

Sane examples of the use of LENGTH are given in Section Cl. 5.1. With a terminal, long plots

can be obtained by setting the argument of a LENGTH instruction to a large number.

Restrictions and Informative Diagnostics

(a) The argument (n) must be positive. If it is zero, negative or not soecified, the

following informative diagnostic is printed:

THE INSTRUCTION WAS IGNORED BECAUSE...

ITS MEANING IS NOT CLEAR.

(b) If the argument (n) is greater than 50 and an INTERACTIVE or TERMINAL instruction has

not been used, the following informative diagnostic is printed:

LENGTH = (n) IS TOO LARGE AND IS RESET FOR NORMAL BATCH USE

Afterwards, a PRINT instruction will print a maximum of 50 lines of numbers and a PLOT

instruction will have 50 intervals on the vertical axis. These are the normal conditions

set by the CMNITAB control instruction.

(c) If the argument (n] of LENGTH is less than 15 and a CPLOT, NCPLOT NPLOT, PAGE PLOT, or PLOT instruction is used, the following informative diagnostic message is printed.

THE INSTRUCTION WAS IGNORED BECAUSE...

LENGTH = (n) IS TOO SMALL FOR A PLOT.

LOCAL printing at terminal $ no arguments

A LOCAL instructions is used to countermand a REMOTE instruction, described below, and

force all printing to be done at the terminal.

REMOTE printing on high-speed printer $ no arguments

19
When using OMNITAB from a terminal, all printing performed by OMNITAB is done at the

terminal. Sometimes, a set of instructions will produce a large amount of printing, in

which case it may be advantageous to use a terminal to write and correct, if necessary, a

set of instructions, but have the printing done on a high-speed printer. This is done

simply by using a REMOTE instruction. The effect of using a REMOTE instruction can be

negated by using a LOCAL instruction.

TERMINAL $ no arguments

A TERMINAL instruction is equivalent to the use of

INTERACTIVE 72

BRIEF

and in addition suppresses the printing of the LIST OF INSTRUCTIONS, DATA, AND DIAGNOSTICS

and the MESSAGE after the complete set of instructions has been executed.

The TERMINAL instruction is intended for interactive use with a terminal or similar

peripheral device. Its use in the batch mode is possible, but unwise since the LIST OF

INSTRUCTIONS, DATA, AND DIAGNOSTICS would not be printed. Caution, a TERMINAL instruction

countermands any previous WIDTH instructions.

WIDTH set to a maximum of (c) characters per line

The maximum number of characters (including blanks) which is printed on any one line is

(c) . This instruction is normally used when instructions are typed on a terminal, but it can also be used to adjust the maximum number of columns printed on a page and the size of

plots.

The OMNITAB instruction sets c = 120 for a high speed printer. An INTERACTIVE

instruction, without any arguments, and TERMINAL instruction set c = 72. The INTERACTIVE

instruction with the argument (c) puts WIDTH (c) into effect.

A WIDTH (c) instruction determines the maximum number of columns printed on a page by

PRINT as follows:

CO Number of columns

15 to 29 1 30 to 44 2 45 to 59 3 60 to 74 4 75 to 89 5 90 to 104 6 105 to 119 7 120 8

Thus, if (c) is 72, up to four columns per page will be printed.

The instruction is negated by an INTERACTIVE instruction and hence should be used after

the INTERACTIVE instruction which normally follows the OMNITAB instruction. It has no

effect on print instructions which have a formal qualifier such as PRINT "L", ABRIDGE "L",

etc., nor does it control the line width for any of the WRITE TAPE or WRITE UNIT

instructions . Also, it has no effect on the printing of error messages or the LIST OF

COMMA DATA AND DIAGNOSTICS. Calcomp Plots are not affected by the use of a WIDTH

instruction >ince the size of plots is determined directly by the CALCOMP instructions used.

20
The minimum number value of (c) allowed is 15 and the maximum value allowed is 120. If

a value of (c) smaller than the allowed minimum is used or a value larger than the allowed

maximum is used, the informative diagnostic:

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

WIDTH = (n) IS TOO SMALL OR TOO LARGE

.

is printed and the number of characters per line will be 120 if the INTERACTIVE has not been

used and 72 if the INTERACTIVE instruction has been used.

The size of graphs produced by PLOT instructions is changed in the horizontal direction

by a WIDTH or INTERACTIVE instruction. The number of divisions of the horizontal scale is a suitable multiple of ten, plus one for an end point, so that the plot is contained in the

specified width. The WIDTH instruction can thus be used very effectively to obtain plots of

different sizes. A table of the number of plotting positions in the x-direction is given

below

.

(c) of WIDTH (c) Number of plotting positions, including end points

59 to 68 41

69 to 78 51

79 to 88 61

89 to 98 71

99 to 108 81

109 to 118 91

119 to 120 101

Note, that if (c) is less than 59 neither PLOT nor PAGE PLOT will be executed and the

following informative diagnostic will be printed:

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

WIDTH = (n) IS TOO SMALL OR TOO LARGE

.

The size of plots produced by PAGE PLOT is unchanged if (c) is greater than or equal to

80. If it is less than 80, the above remarks for PLOT hold and if (c) is also greater than

58, the following informative diagnostic is printed:

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

WIDTH = (n) IS INSUFFICENT FOR PAGE PLOT. BEST PLOT GIVEN.

1.1.2 Use Of Labels.

ALABEL, LABEL, MLABEL

A LABEL instruction makes it possible to use labels in instructions instead of column

numbers. With the ALABEL and MLABEL instructions it is easy to use a label instead of four

(or two) arguments to specify an array or matrix.

Degrees Celsius are computed from degrees Fahrenheit using the relation C = (F-32)/1.8.

If the Fahrenheit degrees are in column 1, then a set of OMNITAB instructions to perform

this calculation could be

HEAD column 1/ FAHRENHEIT

HEAD column 3/ CELSIUS

SUBTRACT 32.0 from column 1 and put in column 2 DIVIDE column 2 by 1.8 and put in column 3 PRINT columns 1 and 3

These five instructions are now exactly equivalent to

21
LABEL FAHRENHEIT, STORE, CELSIUS

SUBTRACT 32.0 from FAHRENHEIT and put in STORE

DIVIDE STORE by 1.8 and put in CELSIUS

PRINT FAHRENHEIT and CELSIUS

Column 1 is automatically used for FAHRENHEIT, column 2 for STORE, and column 3 for CELSIUS.

The words FAHRENHEIT and CELSIUS are automatically used for column headings when the data

are printed. It is also possible to specify the column associated with a label.

Types of labels .

A label may have anywhere from 1 to 12 characters . A blank (space) counts as a character. The first character of a label must be either a letter or a number. If a label

has only one character, that character must be a letter (from A to Z)

.

The characters (,) and currency symbol ($) cannot be used in a label. A label cannot

consist entirely of numbers. As indicated in the description of LABEL below, a minus sign

(-) can be used to imply through as in X-Z. Hence, a minus sign should not be the second

character of a label.

Correct labels

TEMPERATURE

METHOD ONE

A

X+l

X(l)

Use of labels in instructions .

In the ALABEL, LABEL, and MLABEL instructions, labels must be separated by commas. No

other character should be used as a separator, because all the other characters can be part

of a label. No descriptor words are allowed in any of these instructions. The instructions

may appear anywhere in a set of instructions ; except they must follow' the OMNITAB and

DIMENSION instructions. An ALABEL, LABEL, or MLABEL instruction cannot be stored.

When a label is used in any other instruction it must be preceded and followed by one or

more blanks . Labels cannot be erased. Although a label can Be reassigned to another

column, array, or matrix by using a new' ALABEL, LABEL, or MLABEL instruction.

In NBS Technical Note 552 commas are often used after arguments in parentheses for

clarity, but they are not needed in an instruction. Below, commas are put inside

parentheses to indicate that they are an essential part of the instruction.

When a LABEL (or ALABEL or MLABEL) instruction is used, it is not necessary to use the

label in any instruction. The column number assigned to a label by OMNITAB either

indirectly or directly (column number follows label in LABEL instruction) and the label can

be used interchangeably. For example, the set of instructions:

LABEL TEMPERATURE, PRESSURE, YIELD

READ 1 *** 3 (data)

MULTIPLY column 2 by 100.0 and put in col 4

is equivalent to the instructions

:

LABEL TEMPERATURE, PRESSURE, YIELD

READ TEMPERATURE *** YIELD

(data)

MULTIPLY PRESSURE by 100.0 and put in col 4

Incorrect labels

THERMODYNAMICS

X, Y AND Z +X-l

1976

22
When many column headings are desired, one or a few LABEL instructions can be used

instead of a lot of HEAD instructions.

ALABEL (label,) (R, ) (C,) (r,) (c,), (label,) (R,) (C,) (r,) (c,), ...

Each label in the instruction is followed by exactly four arguments:

(R) = the row locating the number in the upper left hand comer of the array

(C) = the column locating the number in the upper left hand comer of the array

(r) = the number of rows in the array

(c) = the number of columns in the array.

These four arguments must be separated by commas; except an x may be used instead of a comma between the third and fourth arguments. A comma should not be placed at the end of the

instruction.

When writing array (or matrix) operation instructions, it is common to put the word size

before (r) x (c). Be sure not to do this in an ALABEL (or MLABEL) instruction, as descriptors are not allowed.

LABEL (label,) (label,) (label,) ...

Column numbers are automatically assigned to the labels. The first label is associated

with column 1, the second label is associated with column 2, and so on.

Expressions such as A-Z or S-W can be used to create labels,

denote through. Hence,

LABEL A-E

The minus sign is used to

is equivalent to

LABEL A, B, C, D, E

Note, only a single letter is allowed. E.g., D-G is correct, but DD-GG is interpreted as a single label. The minus sign must be the second character and should not be followed by a blank space.

A LABEL instruction automatically uses labels for column headings. The label is

centered. If the label has an odd number of characters, there will be one more blank on the

left than on the right. The (first LABEL) instruction

LABEL METER

automatically invokes

HEAD column 1/AAAAMETERAAA

where A is used to denote a blank space.

/

/ LABEL (label,) (C , ) , (label,) (C,), (label,) (C,) ...

/

In this form of LABEL, the column numbers are assigned by the user instead of being

automatically assigned by OMNITAB. Any argument (C) may be deleted and the column number

will be assigned by QMNITAB, using the smallest column number available.

/

/

/

23
If a minus sign is used to denote through, as in A-E, the second letter may be followed

by one or more arguments, separated by commas. If a single argument is used, consecutive

column numbers will be assigned to the labels, starting with the specified column number.

When more than one argument is used, the arguments will be used to assign column numbers to

the labels. If the number of arguments is less than the number of labels, the smallest

column numbers available will be assigned to the remaining labels by OMNITAB.

In the first LABEL instruction

LABEL ALPHA, 30, S-V, 25, DATA SET 2, B-D, 10, 15, 13, 2X, J-M

column numbers will be assigned to labels as follows:

label column number

ALPHA 30

S 25

T 26

U 27

V 28

DATA SET 2 1 B 10

C 15

D 13

2X 2 J 3 K 4 L 5 M 6

MLABEL (label,) (R,) (C,) (r,) (c,), (label,) (R,) (C,) (r,) (c,), ...

The MLABEL instruction is synonymous with the ALABEL instruction described above.

Diagnostics .

(1) An informative diagnostic is always printed which shows the column, array, or matrix associated with each label. For arrays and matrices, four numbers are given. Two

numbers are given for the position of the number in the upper left hand comer, or pivotal

point, and two numbers are printed for the size. Labels, column numbers, pivotal points and

sizes of arrays and matrices are enclosed in parentheses. For example:

LABEL X, TEMP ,17 , METHOD, R-T

* INFORMATIVE DIAGNOSTIC FOR ABOVE INSTRUCTION

(X) IS IN COL (1) , (TEMP) IS IN COL (17)

(METHOD) IS IN COL (2) , (R) IS IN COL (3)

(S) IS IN COL (4) , (T) IS IN COL (5)

ALABEL U TIMES V, 2, 3, 4x6, X+Y, 13, 21, 8x9

* INFORMATIVE DIAGNOSTIC FOR ABOVE INSTRUCTION

(U TINES V) IS IN ROW (2) OF COL (3) SIZE (4X6)

(X+Y) IS IN ROW (13) OF COL (21) SIZE (8X9)

(2) If a HEAD instruction is used for a column which is assigned to a label, the

following informative diagnostic is given:

24
THE INSTRUCTION WAS IGNORED BECAUSE . .

.

(3) COLUMN WAS HEADED BY LABEL INSIRUCTION.

If more than 50 labels are used, the following informative diagnostic is given:

MORE THAN 50 HEAD COLUMN INSTRUCTIONS AND/OR LABELS

HAVE BEEN USED. ONLY THE LAST 50 HAVE BEEN RETAINED.

The first labels (or headings) in are the first out.

Fatal errors .

(1) The following fatal error message is printed if a label is a number:

LABEL OF A COLUMN (ARRAY, MATRIX) CANNOT BE A NUMBER.

(2) The following fatal error message is printed if a label has more than 12 characters

:

A LABEL MUST HAVE AT LEAST 1 AND LESS THAN 13 CHARACTERS

(3) If two labels are assigned to the same column or if two arrays (matrices) are assigned the same pivotal point, the following fatal error message is printed:

COLUMN (ARRAY, MATRIX) HAS BEEN BEEN PREVIOUSLY LABELED.

No check is made to determine whether a label has been used more than once. If a label is used more than once, the last use will determine the column number or pivotal point in use.

(4) If the number of labels exceeds the number of columns in the worksheet, the

following fatal error message is printed:

NUMBER OF LABELS EXCEEDS (n) COLUMNS IN WORKSHEET.

where (n) is the number of columns in the worksheet.

(5) If a label in ALABEL or MLABEL is followed by less than four arguments, the

following fatal error message is printed:

LABEL IN ALABEL OR MLABEL MUST BE FOLLOWED BY 4 NUMBERS

.

This error can occur if the commas between the arguments are accidently omitted or

if a comma is not put after any label.

Caution.

Instructions can now be written in a more natural form. Descriptors are still allowed.

However, care should be exercised to avoid using a descriptor when a label should be used or

vice versa.

Care should be exercised in using a single letter as a label, because the label might be

interpreted as a format, unit, or variable qualifier. In

LABEL A, Y

READ A, Y

the A in the second instruction is considered a qualifier, not a label. If the letter A in

the READ instruction is meant to be a label, one or more descriptors should be inserted

before it, as in

25
LABEL A, Y

READ data into A and Y

1.1.5 Multilingual OMNITAB II.

DANSK, DEUTSCH, ENGLISH, ESPANOL, FRANCAIS , ITALIANO, JAPANESE,

NEDERLANDS, NORSK, PORTUGESE, SLOVENE, SVENSKA, YUGOSLAV, VOCABULARY

Twelve instructions were added so that instructions can be written in languages other

than English. It is possible to switch from one language to another by simply inserting the

appropriate command. Hence, the command ENGLISH is also given to return the system to its

normal mode of operation. An additional command, VOCABULARY, prints the first six letters

of each word of every command in the system for both English and the last language specified

by one of these commands.

Translations for some of the languages have not been made. For other languages, the

translation is tentative and subject to revision. Updating of translations will not, in

general, keep up with the addition of new instructions. In all cases, translations are not

given for the magnetic tape instructions or the Bessel function instructions.

1.3* Common Printing Instructions .

A FIXED instruction with argument zero has been modified to print integers without the

decimal point. Zero is now printed without a zero before the decimal point when FIXED (n)

is used and n is greater than zero.

1.5.1 Extended Plotting Capability .

CPLOT, FOURPLOTS, NCPLOT, NPLOT, TWOPLOTS

CPLOT col (C) , symbol (E)...col (C) , symbol (E) versus column (C)

A CPLOT instruction is similar to a PLOT instruction; except the plotting symbol is

specified instead of being automatically assigned. Much greater flexibility is available in

the use of plotting symbols.

The argument which specifies the plotting symbol always is placed immediately after the

column number designating the variable for the vertical axis. The argument can be either a constant or a column number. If it is a constant, the specified symbol is used for each

point on the curve. If the argument is a column number, the symbol specified in row one is

used to plot the point in row one, the symbol specified in row two is used to plot the point

in row two, and so on. This use of CPLOT can be very powerful. If the points fall into

logical subsets such as for different laboratories, different test methods, etc., then a different plotting symbol can be used for each subset.

26
Numbers are used to indicate the required plotting symbol in accordance

following table.

Symbol Constant Symbol Constant Symbol Constant

0 0 G 17 W 33

1 1 H 18 X 34

2 2 I 19 Y 35

3 3 J 20 Z 36

4 4 K 21 / 37

5 5 L 22 . 38

6 6 M 23 - 39

7 7 N 24 + 40

8 8 0 25 * 41

9 9 P 26 ( 42

0 10 Q 27 ) 43

A 11 R 28 » 44 B 12 S 29 blank 45

C 13 T 30 = 46

D 14 U 31 $ 47

E 15 V 32 t 48

F 16

Note, 0.0 or 10.0 can be used for the symbol zero. Whenever more than one point falls

on the same plotting position, the last symbol is used.

If the number specifying a plotting symbol is less than zero or greater than 48, the

CPLOT instruction will be executed as a PLOT instruction.

There are optional forms of CPLOT analogous to the optional forms of PLOT, as shown

below.

/ / / CPLOT col (C) sym (E) ,

. .

.

,

(C) (E) vertical scale from (K) to (K) vs col (C) /

/ /

/

' / / CPLOT col (C) sym (E) ,

. .

.

,

(C) (E) vs col (C) horizontal scale from (K) to (K) /

/ /

/

“7

/ CPLOT (C) (E) ,

. .

.

,

(C) (E) vertical (K) to (K) vs (C) horizontal (K) to (K) /

/ /

Examples

(a) The following set of instructions is used to illustrate the use of CPLOT, NCPLOT

described below, and LENGTH described in Section Cl. 1.1. The results are shown on page 29.

27
OMNITAB 2/18/77 EXAMPLE OF USE OF CPLOT AND NCPLOT

LABEL X, SINE, COSINE

GENERATE -4. (.2) 4.0 IN X

SIN OF X PUT IN SINE

COS OF X PUT IN COSINE

TITLEY SIN X COS X

TITLEX GRAPH 1 2/3/77

WIDTH 75 LENGTH 30

CPLOT SINE WITH SYMBOL 11. AND COSINE WITH SYMBOL 12. VS X FROM -2.5 TO +2.5

SPACE 5 WIDTH 90

LENGTH 20

TITLEY SIN X COS X

TITLEX GRAPH 2 2/3/77

NCPLOT SINE WITH SYMBOL 1. AND COSINE WITH SYMBOL 3. VS X FROM -3.0 TO +3.0

STOP

(b) The following table gives door temperatures (65" above floor) for five different

types of fabric and four curtain conditions.

Fabric

Closed Open

Light Heavy Light Heavy

1 52 86 41 75

2 134 171 44 122

3 124 306 91 249

4 83 121 39 41

5 116 - 39 -

These data can be analyzed formally using an analysis of variance. On the other hand, a graphical analysis can be revealing and often more easily understood. A different symbol can be used to plot door temperature versus fabric number for each of the four curtain

conditions: closed curtain of light fabric, closed curtain of heavy fabric, open curtain of

light fabric and closed curtain of heavy fabric. A set of instructions, using CPLOT, for

making a graphical analysis is:

OMNITAB 2/19/77 PLOT OF FABRIC FLAMMABILITY DATA

SET DOOR TEMPERATURES IN COLUMN 1 52, 134, 124, 83, 116, 86, 171, 306, 121,

41, 44, 91, 39, 39, 75, 122, 249, 41.

$SET CURTAIN CONDITION IN COLUMN 2

1 , 1 , 1 , 1 , 1 , 2 , 2 , 2 , 2 , 3, 3, 3, 3 3 4 4 4 4. ADd’iO.o’tO COLUMN’ 2 AND PUT ’ IN COLUMn’2

$

SET TYPE OF FABRIC IN COLUMN 3

1, 2, 3, 4, 5, 1, 2, 3, 4,

1, 2, 3, 4, 5, 1, 2, 3, 4.

$

WIDTH 80

LENGTH 45

CPLOT COL 1 WITH SYMBOL IN COL 2 FROM 0.0 TO 400.0 VS COL 3 FROM 0.0 TO 6.0

STOP

The result is shown on page 30. Connecting like symbols by hand with colored straight lines

makes the plot much easier to interpret.

28
OMNITAB 2/18/77 EXAMPLE OF USE OF CPLOT AND NCPLOT PG 1

ABS- X ,ORD- SINE (A), COSINE (B)

,

POINTS PLOTTED 50 POINTS NOT PLOTTED - OUT OF BOUND 32

+ + + x + — + + 1.0000+ B B B AAA + B B A A - B BA A - B B A - S - A - I - B B - N B A B

B B

. 00021323X B

B

B

- B

- B

A

A B -

A A

-.99957+ AAA +

-2.5000 0. 2.5000

GRAPH 1 2/3/77

ABS- X ,ORD- SINE (1), COSINE (3),

POINTS PLOTTED 62 POINTS NOT PLOTTED - OUT OF BOUND 20

+ + + + x + +- S 1.0000+

IN 3 3 33333 11111

3 3 11

13 3

1 3

C -1 O - 1 S

X -.99957+3 3 + -3.0000

113

3 3 1 111 11

.+ +-

1 1

-1.2857

1 1

3 3

-- + x + +

0. 1.2857

GRAPH 2 2/3/77

1-X

3 3+ —

+

3.0000

29
OMNITAB 2/19/77 PLOT OF FABRIC FLAMMABILITY DATA PG 1

ABS- COLUMN 3 ,ORD- COLUMN 1 ( ),

POINTS PLOTTED 18 POINTS NOT PLOTTED - OUT OF BOUND 0 + + + + + —+ + 400.00+ +

-

B

-

300.00+ +

- D

-

200.00+ +

- B

_

- A

-

- D A B A -

100.00+ +

- B C

- - A -

- D -

- A _

- C C D C

-

o.x X

+ — + —+ —+ + —+ +

0. 2.0000 4.0000 6.0000

30
FOURPLOTS of (C) vs (C) and (C) vs (C) and (C) vs (C) and (C) vs (C)

The instruction allows four plots to be printed on a single page, instead of on four

separate pages. This makes a visual comparison much easier and conserves paper. Each plot

has 21 by 51 divisions or 20x50 intervals. A title

PLOT OF xxxxxxxxxxxx VERSUS xxxxxxxxxxxx

appears at the top of each plot, where xxxxxxxxxxxx is either COLUMN followed by the

appropriate column number or the column heading if a HEAD or LABEL instruction has been

used. The number of points plotted and the number of points not plotted because they are

out of bounds is printed at the bottom of each plot. The plotting symbol is a period (.).

If two or more points coincide, the plotting symbol is an asterisk (*)

.

If the value of WIDTH, as determined by a WIDTH, INTERACTIVE or TERMINAL instruction, is

less than 120, the first two plots are printed, one below the other, on one page and the

last two plots are printed, one below the other, on the next page.

The first column number, (C) , in any pair is alwrays used for the vertical axis variable

and the second column number, (C) , in any pair is always used for the horizontal axis

variable

.

/ /

/ FOURPLOTS (C) , (K) , (K) , (C) , (K) , (K) , (C) , (K) , (K) , (C) , (K) (K) (C) , (K) , (K) , (C) , (K) , (K) /

/ /

This form of FOURPLOTS is the same as the one above except; the upper and lower limits

of the vertical and horizontal axis can be specified by a pair of constants (K) , (K) . Any

of the eight pairs of limits, (K) (K) , may be omitted or inserted. The instructions:

LABEL REL. INCOME, 12 , FOURPLOTS 11 vs 12, 11, 30., 40. vs 13, 11 vs 14, 140., 190., 12, 9., 11.5 vs 13, 10., 35.

used with the data on page 157 of NBS TN 552 produce the results shown on page 33.

NCPLOT (C) , (E) , . .

. , (C)

,

(E) versus column (C)

A NCPLOT instruction has exactly the same relation to a CPLOT instruction as a NPRINT

instruction has to a PRINT instruction. In other words, a NCPLOT instruction does not plot

on a new page. Clearly, it should be used in conjunction w

r ith a LENGTH instruction. The

optional forms of PLOT exist for NCPLOT as shown below-.

/ 7

/ NCPLOT (C) , (E) , . .

. , (C) , (E) vertical scale (K) to (K) vs col (C) /

/ /

/ /

/ NCPLOT (C) , (E) , . .

. , (C) , (E) vs col (C) horizontal scale (K) to (K) /

/ /

/ /

/ NCPLOT (C) , (E) , . .

. , (C) , (E) vertical (K) to (K) vs (C) horizontal (K) to (K) /

/ /

31
NPLOT columns (C) , (C) ,

. .

.

,

(C) versus column (C)

A NPLOT instruction has the same relation to a PLOT instruction as a NPRINT instruction

has to a PRINT instruction. A NPLOT instruction does not print the plot on a new page. It has no use unless a LENGTH instruction is used with an argument less than 50. If a LENGTH

instruction is used it is possible to get more than one plot on the same page. The optional

forms of PLOT exist for NPLOT. A maximum of five curves can be plotted at one time.

/ NPLOT cols (C) , (C) ,

. .

.

,

(C) vertical scale from (K) to (K) versus col (C) /

/ /

/ 7

/ NPLOT cols (C) ... (C) versus column (C) with horizontal scale from (K) to (K) /

/ /

/ /

/ NPLOT cols (C) ... (C) vertical (K) to (K) vs col (C) horizontal (K) to (K) /

/ /

TWOPLOTS of column (C) versus column (C) and column (C) versus column (C)

A TWOPLOTS instruction prints two plots in the upper half of a page, instead of on two

separate pages. Each plot has 21 by 51 divisions or 20 by 50 intervals. A title

PLOT OF XXXXXXXXXXXX VERSUS XXXXXXXXXXXX

appears at the top of each plot, where XXXXXXXXXXXX is either COLUMN followed by the

appropriate column number or the column heading if a HEAD or LABEL instruction has been

used. The number of points plotted and the number of points not plotted because they are

out of bounds is printed at the bottom of each plot. The plotting symbol is a period (.).

If two or more points coincide, the plotting symbol is an asterisk (*)

.

If the value of WIDTH, as determined by a WIDTH, INTERACTIVE or TERMINAL instruction, is

less than 120, the two plots are printed one below the other on the same page.

The first column number , (C)

,

in either pair is always used for the vertical axis

variable and the second column number, (C)

,

in either pair is always used to specify the

horizontal axis variable.

/ — y

/ TWOPLOTS col (C) (K) to (K) vs (C) (K) to (K) and (C) (K) (K) vs (C) (K) (K) / /— /

This form of TWOPLOTS is the same as the one above; except the upper and lower limits of

the vertical and horizontal scales can be specified by a pair of constants. Any of the four

pairs of constants may be omitted or included. The instructions

LABEL REL. INCOME, 12 TWOPLOTS of col 12 versus col 14 and col 13 vs col 14

used with the data on page 157 of NBS TN 552 produce the result shown on page 34.

32
PLOT

OF

COLUMN

11

VERSUS

REL.

INCOME

PLOT

OF

COLUMN

11

VERSUS

COLUMN + LO I to

33 9

POINTS

PLOTTED 0

POINTS

OUT

OF

BOUNDS

NOT

PLOTTED 7

POINTS

PLOTTED 2

POINTS

OUT

OF

BOUNDS

NOT

PI
OMNITAB

2/12/71

DRAPER

AND

SMITH

EXAMPLE

ON

PAGE

216

34
1.5.2 Use Of Calcomp Plotter .

CALCOMP AXIS, CALCOMP FAST, CALCOMP PAPER, CALCOMP PLOT,

CALCOMP SIZE, CALCOMP SLOW, CALCOMP SPEED, CALCOMP TAPE

This section is only applicable if a Calcomp Digital Incremental Plotter and associated

software are available.

The eight Calcomp instructions allow one to plot data using the Calcomp Digital

Incremental Plotter. Since the Calcomp Plotter at MBS is not directly accessed from the NBS

computer, special procedures explained below are necessary to have graphs processed. The

instructions for plotting have two word commands. The first word is always CALCOMP and the

second word specifies plotting conditions. The instruction CALCCMP PLOT is the basic

instruction and the remaining seven instructions provide additional flexibility by taking

advantage of the special features of the Calcomp Plotter.

Calcomp plots have superior quality compared to those produced by the PLOT instruction

on the high-speed line printer (or terminal), but are more expensive. Unless you are sure of the form a plot should have, it would be wise to obtain one or more rough plots using the

PLOT instruction before using the Calcomp Plotter.

This section describes (i) how to obtain a Calcomp plot, (ii) the basic CALCOMP PLOT

instruction (with variations), (iii) the use of titles, (iv) how to change the size of the

plotting paper or axes, (v) how to change the speed of the plotting pen, (vi) how to use

tape units, (vii) diagnostics, and (viii) an example. Upon first reading, item (i), the

first part of item (ii) and (viii) need only be read. The remaining material provides great

flexibility for more sophisticated use. Additional information is given in the NBS Computer

User's Guide and in "Programming for Calcomp Digital Incremental Plotters," Bulletin No. 170 -E/November 1966, California Computer Products, Inc. 305 N. Muller St., Anaheim, CA

92803.

How To Obtain A Calcomp Plot .

Since the Calcomp Plotter at NBS is not directly accessed from the NBS computer, the

information for the Calcomp Plotter is generated on magnetic tape and plotted at a later

time. Four steps are required.

(1) Obtain tape reel.

Call computer desk on extension 3467 to have a tape number assigned to you. The tape may be labeled or unlabeled. The use of labeled tapes is

encouraged

.

(2) Control cards for tape use. The following two control cards should appear immediately after the RUN

card.

@MSG,W TASKNO- NAMEXX, WRITE ENABLE TAPE yyyy

@ASG,T x.,8C,yyyyW

The symbol x stands for an integer from 7 through 12. If a CALCCMP TAPE

"L" instruction is not used, set x equal to 7. Otherwise, x is 7, 8, 9,

10, 11 or 12 depending upon whether the qualifier "L" is A, B, C, D, E or F. The expression yyyy stands for the four digit magnetic tape reel

number assigned to you. Note; the symbol J should follow T, if an unlabeled tape is being used. None of the TAPE or UNIT instructions

should be used with a Calcomp tape.

(3)

Instructions

.

Write your set of OMNITAB II instructions using as many CALCCMP PLOT

instructions as you wish and any of the other Calcomp instructions if

35
you so desire. Then send your set of instructions to the computer as usual

.

(4) Send tape to Calcomp Plotter.

The instructions in (3) above do not automatically provide a plot.

After you receive your output containing the LIST OF COMMANDS, DATA AND

DIAGNOSTICS the tape must be sent to the Calcomp Plotter. To do this,

complete Form NBS-777 as usual and in addition fill in the information

for Calcomp Plots on the right, as indicated in the example on page 41.

Color is normally black, but red is good for copying. Pen is usually

ballpoint. Paper is either 12 or 30 inches high. (See Size Of Paper

below.) Frame count is the number of graphs to be plotted. Put the

reel number in the TAPE(S) box. The use of other options may be

discussed at the Computer Services Division Consultant Desk.

Basic Calcomp Instruction .

The eight forms of the CALCOMP PLOT instruction generate the data to plot the graph.

The different forms allow the user (i) to specify the scale of the vertical and/or

horizontal axes, (ii) to plot a number of curves against a single abscissa, and (iii) to

plot curves with independent abscissas (i.e. each curve defined by two arguments - an ordinate and an abscissa). A new graph is generated with each CALCOMP PLOT instruction.

The first two arguments of all forms of the CALCOMP PLOT instruction are the same. The

first argument, an integer number, indicates the number of curves to be plotted for that

graph. The second is either a column number or a constant with a decimal point. This

argument describes the options to be used for plotting the curves. If the argument is a constant, then the option applies to all the curves on that graph. However, if the argument

is a column number, then the option in row 1 of that column applies to the first curve,

option in row 2 of that column to the second curve, and the option in row n to the nth

curve.

Graphs are enclosed on four sides and ticked at one inch intervals . Values are printed

at the tick marks along the left vertical and bottom horizontal boundaries. The numbers are

in readable format. Three significant digits are used if the absolute value of the numbers

is less than or equal to 100. If the absolute values exceed 100, the number of significant

digits varies from 3 to 6.

CALCOMP PLOT (n) curves (E) options columns (C) , (C) , . .

. , (C) against (C)

Generates a graph of (n) curves with the same abscissa, defined by the last column.

Both the horizontal and vertical scales are determined by OMNITAB. The maximum number of

curves per graph is 97.

The general form of the option is

X.YYZZ

where X is any value 1 through 9, YY takes on values 00 through 85, and ZZ values are 00

through 99.

The value of X indicates whether symbols are to be used and how the points are to be

joined (if at all) as indicated in the following table:

36
X Use Symbols Join Points

1

2 yes no . 3

4 yes no . 56 yes no . 7

8 yes no . 9 yes

no

yes

yes

. dash lines

. dash lines

smooth curve

smooth curve closed smooth curve closed smooth curve

If X is 2 or 3, straight lines are drawn from point to point. For X=6,7,8 or 9, a curve is

fitted through the points (using a modified spline fitting technique). Furthermore, the

first and last point are joined for X = 8 or 9.

YY defines the particular symbol of the 85 available to be used for labeling points on

the curve. If X is odd and YY is 00, then the first curve that requires a label will be

labeled with the first symbol in the table below, the nth curve with the nth symbol. The

first 15 symbols (01-15) are centered symbols, the rest (16-85) are lower left

symbols. The values of YY and corresponding symbols are:

Symbol YY Symbol YY Symbol YY

A 16 0 42 -> 52 B 17 1 43 t 53

C 18 2 44 I 54

D 19 3 45 > 55

E 20 4 46 <T 56 F 21 5 47 57

G 22 6 48 A 58

H 23 7 49 + 59

I 24 8 50 i 60

J 25 9 51 [ 61

K 26 ] 62

L 27 # 63

M 28 A 64

N 29 Centered ) 65

0 30 66 P 31 01 + 67

Q 32 © 02 < 68

R 33 A 03 > 69

S 34 + 04 0 70

T 35 X 05 $ 71

U 36 <!> 06 * 72

V 37 + 07 ( 73

W 38 X 08 0, 0 74

X 39 z 09 \ 75 Y 40 Y 10 ? 76

Z 41 w 11 1 77

12 f 78

X 13 \ 79

14 t 80

s 15 81

/ 82

83

ft 84

7 85

37
Every ZZth point on the curve will be labeled with the symbol requested. If ZZ is not

defined (i.e., ZZ = 00) and labeling is requested, every point on the curve is labeled.

Thus to label every 5th point on the curve, 05 should be the value of ZZ.

/ CALCOMP PLOT (n) , (E) vertical scales (K) to (K) , (C) , (C) , . .

. , (C) vs (C)

/

"7

/

/

This option is the same as the first except the user defines the range of the vertical

scale (ordinate). The scale need not be increasing, i.e., the first (K) which specifies the

value at the bottom of the graph can be greater than the second (K) which specifies the

value at the top of the graph. The horizontal axis is determined by the instruction.

/ CALCOMP PLOT (n) , (E) , (C) , . .

. , (C) vs (C) horizontal scales from (K) to (K) /

/ /

The user selects the horizontal scale (abscissa) while the instruction determines the

vertical scale.

/

/ CALCOMP PLOT (n) , (E) , ordinate (K) , (K) , (C) . . . (C) vs (C) abscissa (K) , (K) /

The user selects the horizontal and vertical scales. All points which fall outside the

range are not plotted.

*****

The next four forms of the CALCOMP PLOT instruction are the same as the previous forms;

except every curve is defined by a pair of arguments (column numbers) . For each curve that

is to be plotted, an ordinate and an abscissa are given. These forms make it possible to

plot curves with unrelated abscissas on the same graph. The maximum number of curves per

graph is 49 if these options are used. Warning : the number of points for each curve must be

the same, otherwise extraneous points will be plotted for some curves.

! /

/ CALCOMP PLOT (n) curves , (E) options (C) vs (C) , (C) vs (C) , . .

. , (C) vs (C) /

/ /

/ /

/ CALCOMP PLOT (n) , (E) , vertical scale (K) , (K) , (C) vs (C) (C) vs (C) /

/ /

/ 7

/ CALCOMP PLOT (n) , (E) , (C) vs (C) , . .

. , (C) vs (C) horizontal scale (K) , (K) /

/ /

/ /

/ CALCOMP PLOT (n) , (E) vertical (K) , (K) , (C) vs (C) , . .

. , (C) vs (C) horizontal (K),(K)/

/__ / *****

38

I
Titles

.

At the top of every graph drawn by the Calcomp Plotter, the OMNITAB card is printed. On

the next line, GRAPH (m) is printed, where (m) is the number printed after the CALCOMP PLOT

instruction. The instructions TITLEX and TITLEY (Section C 1.5) may be used to label the

horizontal and vertical axes. The information provided by TITLEY is printed on the left of

the y-axis (vertical), if this instruction is used. The information supplied by TITLEX, if

any, appears at the bottom of the x-axis (horizontal).

Size Of Plotting Paper And Axes .

Chart paper roll of 120 feet in length with a plotting height (vertical axis) of 12 or

30 inches is available on the Calcomp Plotter. Twelve inch height is assumed unless

otherwise specified. The standard size of the graphs is 9 inches in height by 6 inches in

width for a 12 inch paper height and 27 by 18 for 30 inch paper height. Labels and headings

add another lh inches to the height and % inch to the width. TITLEX and/or TITLEY add ^ inch more in either direction.

There are three instructions available to change the standard (i.e., assumed) height and

width of the axes of a graph and the height of the plotting paper.

CALCOMP AXIS (K) height of y-axis, (K) width of x-axis

The height of the y-axis and width of the x-axis are defined in inches by the two

arguments. If the height and/or width is given in fraction of inches, the values will be

rounded to the nearest inch.

CALCOMP PAPER (n) height of paper

If (n) is not 12 or 30, an informative diagnostic is given and 12 inch height paper is

assumed. This instruction should only be used once and before any CALCOMP PLOT instruction.

CALCOMP SIZE (n) height of paper

The argument (n) is used to set the height of the paper to either 12 or 30 inches,

form is synonymous with CALCOMP PAPER.

/

/ CALCOMP SIZE (K) height of vertical axis (y-axis)

/

This

/

/

/

The maximum value of (K) is the height of the paper minus 2 inches.

/ /

/ CALCOMP SIZE (n) height of paper (K) height of vertical axis /

/ /

Both the height of paper and vertical axis are defined.

/ /

/ CALCOMP SIZE (K) height of vertical axis, (K) length of horizontal axis /

/ /

The height and length of the graph are given. This form is the same as CALCOMP AXIS.

39
/— /

/ CALCOMP SIZE (n) height o£ paper, (K) vertical height, (K) horizontal width /

/_ /

The user defines the height of the paper and vertical axis and length of the horizontal

axis.

Speed Of Plotting Pen .

The normal plotting speed is fast, but the speed at which the plotting is done can be

controlled by the user using any one of the following three instructions. If one wishes to use ink pens , the plotting should be done in slow mode to permit the ink to dry before the

paper or plotting arm moves. For ball point pen, fast (zip) mode is sufficient. Computer

time to generate the output will not be affected by either mode. However, plotting time on

the Calcomp Plotter will be longer with slow mode.

CALCOMP FAST (zip) mode

Plotting mode is set to fast (zip) speed.

CALCOMP SLOW mode

The plotting arm of the Calcomp Plotter will operate in slow mode to allow the ink to

dry.

The following instruction has the previous two as special cases.

CALCOMP SPEED (n) $ n=0 for slow mode, n=l for fast mode

The speed of the Calcomp Plotter is defined as either slow if n=0, or fast if n=l. The

instruction

CALCOMP SPEED 1

is synonymous with the instruction

and the instruction

CALCOMP FAST

CALCOMP SPEED 0

is synonymous with the instruction

CALCOMP SLOW

Tape Unit .

The CALCOMP TAPE "L" instruction permits the user to direct the output for plotting to any of the logical tape units A through F. If the instruction is not used, logical tape A

is assumed. NBS users must assign a tape file which may be labeled or unlabeled.

40
CALCOMP TAPE "L" unit

The graphs generated by the CALCOMP PLOT instruction are outputed on tape unit "L". The

qualifier "L" is any letter A through F.

Diagnostics .

If the CALCOMP PLOT instruction is successfully executed, the following information will

be printed immediately below the instruction in the LIST OF COMMANDS , DATA AND DIAGNOSTICS:

GRAPH m WAS PLOTTED

where m is an integer indicating the graph number. Because the plotting is done after the

OMNITAB program is executed, this number along with the word GRAPH will be printed on the

top of the graph to identify the CALCOMP PLOT instruction which generated that output for

the Plotter. The user can also determine the number of graphs produced on the magnetic tape

if the OMNITAB program terminates through a fatal error.

In the instructions CALCOMP AXIS and CALCOMP SIZE, the maximum height of the y-axis must

be less than or equal to the height of the paper minus 2 inches. Otherwise the following

informative diagnostic is given

CALCOMP PAPER WIDTH IS NOT 12 OR 30 INCHES,

OR Y HEIGHT IS TOO LARGE.

and the axes are redefined to the standard size (9x6 or 27x18 for 12 or 30 inch paper

respectively). The above diagnostic is also given and 12 inch paper assumed, if the

argument of CALCOMP PAPER is not the integer 12 or 30.

Whenever the information in a TITLEY or TITLEX is too long for a graph, the following

informative diagnostic is given:

TITLEX OR TITLEY TOO LONG FOR GRAPH. TITLE IS OMITTED.

The following informative diagnostic is printed if the argument in the instruction

CALCOMP SPEED is incorrect

:

CALCOMP PLOTTING SPEED SPECIFIED IS NOT CORRECT,

ZIP CODE OR HIGH SPEED IS ASSUMED.

If the option(s) specified in the CALCOMP PLOT instruction is incorrect, the following

informative diagnostic is given:

EITHER SYMBOL OR JOINING SPECIFIED IS INCORRECT.

SYMBOL . OR NO JOINING IS USED.

41
An Example .

The following set of CAIN I TAB instructions produced the two graphs on pages 43 and 44

which show plots of sin(x), cos(x), sin(x)+cos(x) , and the straight line y

= -1 + 0.5x.

OMNITAB EXAMPLES OF CALCQMP PLOT INSTRUCTION

GENERATE NOS -2.0 IN STEPS OF .5 THRU 4. IN COL 1 SIN OF COL 1 PUT IN COL 2 COS OF COL 1 PUT IN COL 3

ADD COL 2 TO COL 3 PUT IN COL 4 MULTIPLY COL 1 BY .5 THEN BY 1.0 ADD -1.0 PUT IN COL 5 GENERATE FROM 1. STEPS OF 1. THRU 4. PUT IN COL 6 RESET NRMAX TO 13

AADD 1,6 lxl .12 PUT IN 1,6

AADD 3,6 lxl .7102 PUT IN 3,6

TITLEY SIN (X): COS(X): SIN(X)+COS(X) : Y=-1+.5*X

TITLEX FIGURE 1: OPTIONS VARY FOR EACH CURVE

CALCOMP SIZE 8. INCHES FOR Y-AXIS 6. INCHES FOR X-AXIS

CALCOMP PLOT 4 CURVES OPTIONS IN 6 Y-LIMITS (-2., 2.) FOR COLS 2 3 4 5 VS 1 TITLEX FIGURE 2 : ONE OPTION FOR ALL CURVES

CALCOMP PLOT 4 CURVES OPTION 7.0103 Y LIMITS(-2. ,2.) FOR COLS 2 3 4 5 VS 1 STOP

Column 6 contains the values 1.1200, 2.0000, 3.7102 and 4.0000 which are the options for

curves 1, 2, 3 and 4, respectively for the first graph. The option 1.1200 requests that the

first curve, sin(x), is to be plotted with no points joined (1), the symbol to be used is * (12) and every point is to be labeled since the last two digits are zero. The cos(x) curve

is drawn with points joined and no labeling. The points of the third curve, sin(x)+cos(x)

,

are to be joined and every second point to be labeled with the symbol $ (71). The straight

line is drawn using dash lines.

Since the option argument in the second CALCOMP PLOT instruction is a constant (7.0103)

all four curves of the second graph are to be drawn using a smooth curve which has every

third point labeled with the symbol [D . Note that the cos (x) curve in graph two is smoother

than the curve in graph one, because points are joined using a smooth curve rather than a straight line.

The completed form NBS-777 used to send the tape (graphs) to the Calcomp Plotter is

shown below.

PR. RUN ID ACCOUNT NO. T 1 ME PAGES CARDS NAME PHONE BIN xxxxx S. Peavy 2315

RE EL NO. WRITE ENABLE PERIPHERAL OPERATIONS

| | MAIL TO: Q] WILL PICK UP

| | SEND TO 2ND FLOOR

| | SEND TO 3RD FLOOR

| | KEYPUNCH Q VERIFY

l 1 LIST CAR!

| | DUPE CAF

-»<; timf«; SC 4020 CALCOMP

DS TIMES | | PRINT MODE

| | PLOT MODE

f~| DUPE FILM

| | 16 MM

| 1 35 MM

| | HARD COPY

INK COLOR black

PEN ball point

| | DIGIDATA

(

1 OTHER

(Specify ) PAPER

12 inch

FRAME tOUNT

2 BRKPTED

REEL NO.

PGS. EST.

CDS.

EST.

NO

R

E

XT PART

PAPER

SPECIAL FORMS

FRAME COUNT TAPE(S) T A PEIS)

SPECIAL INSTRUCTIONS m

| | CONT. ON REVERSE

JOB LOADED

AT . BY

JOB PICKED UP

BY

| | OK TO

RE LOAD

NBS-777 U.S. DEPARTMENT OF COMMERCE

(Rev. 9-73) NATIONAL BUREAU OF STANDARDS JOB REQUEST CARD

DATE

42
SIN

(

X

)

:

C0S(

X

)

:

S

I

N(

X

)+C0S(

X

)

:

0f1N I TfiB CXHCIPLCS 0F CflLC0MP PL0T INSTRUCTION X

*

in

a +

>-

GRRPH 1

FIGURE 1: OPTIONS VARY FOR EACH CURVE

43
SIN

(

X

I

:

C0S(

XI:

S

I

N

(

X

I+C0SI X

I

:

onNiTfls rxfinPLrs or cflLConp plot instruction

GRAPH 2

FIGURE 2: 0NE 0PTI0N F0R ALL CURVES

44
1.6* Optional Forms Of Readable Printing .

The sentence "When numbers are printed as integers, a single blank space appears on the

right." near the end of the fourth paragraph on page 73 of NBS TN 552 is no longer true. The example below should be changed accordingly.

1.7* Formatted Printing And Reading .

Formatted READ with 0 count . It is now possible to read with a zero count using an An

descriptor by inserting a blank card (line) at the end of the data. This is true for a READ

"L" instruction, but is not valid for a READ UNIT "L" "L" instruction.

1.10* Use Of Magnetic Tapes, Drums, Discs, etc .

BACKSPACE UNIT "L", CREAD UNIT "L", CREAD UNIT "L" "L", CSET UNIT "L",

ENDFILE UNIT "L", READ UNIT "L", READ UNIT "L" "L", REWIND UNIT "L",

SET UNIT "L", SKIP UNIT "L", UNIT, WRITE UNIT "L" MITE UNIT "L" "L"

Normally, information is fed to a computer from punched cards or a card image typed on a terminal. The instructions described in this section enable one to use magnetic tapes,

drums, discs, or any other medium. The average user of OMNITAB should seek additional

assistance from his system programmer when working with magnetic tapes or discs. Throughout

this section the word UNIT in an instruction will denote a tape, disc or drum, etc. The

word TAPE can be used instead of UNIT as indicated at the end of this section under

synonyms

.

Each instruction in this section can be stored for repeated use. When tape operation

instructions are incremented, the first qualifier should not be incremented.

Data read from tapes will not be printed with the LIST OF DATA, COMMANDS and

DIAGNOSTICS. Instead, the message

(n) DATA RECORDS READ BUT NOT PRINTED

will follow the CREAD UNIT and READ UNIT instructions and diagnostic:

(n) DATA VALUES READ BUT NOT PRINTED

will follow the CSET UNIT and SET UNIT instructions.

Information on any unit or tape is grouped into logical records within blocks. Logical

records may be thought of as lines on a page and blocks as pages. Unless a UNIT instruction

is used, the number of characters per logical record is 80 and the number of records per

block is 1. This would be the case if one were reading card images from an unblocked tape.

If blocked tapes are used or if more than 80 characters per record are to be read, a UNIT

instruction must be used to indicate the number of characters per logical record and, if

necessary, the number of logical records per block.

System Control Statements

This material is for the benefit of users of the NBS computer. Other users must obtain

comparable information from their computer center.

When using UNIT instructions, @MSG, @ASG and @USE system control statements must be used

for each medium (tape, unit, etc.) assignment. The control cards should come right after

the @RUN statement and have the following form:

45
@MSG,W TASKNO - NAMEXX , WRITE ENABLE TAPE yyyy

@ASG[, options] LABEL. ,xx,yyyyW

@USE N. , LABEL

where yyyy is the tape reel number and xx = 8C if the physical unit is a tape and xx— F if

the physical unit is Fastrand. The MSG control statement and the letter W after the reel

number are only needed if data is to be written on tape. The variable N will be 7, 8, 9,

10, 11, or 12, depending whether the qualifier of the UNIT instruction is A, B, C, D, E or

F.’ For a complete description of the other variables and options on the control statements

refer to sec. 3. 7. 1.1 (for ASG Fastrand medium), sec. 3. 7. 1.2 (for ASG tape medium) and sec. 3.7.5 (for USE) of UNIVAC 1100 Series Operating System Programmer Reference (UP-4144 Rev.

3).

Instructions.

BACKSPACE UNIT "L" (r) logical records

This instruction backspaces unit "L" by (r) logical records. If a UNIT instruction has not

been used, the instruction backspaces (r) blocks. However, if a UNIT instruction was used,

the number of blocks that will be backspaced is:

r/r' + m,

where r' is the number of logical records per block and:

m = 1, if r/r' has a remainder

m = 0, if r/r' has no remainder.

The unit is always positioned at the beginning of a block.

CREAD UNIT "L", using (r) logical records, into columns (C) , (C) , ... (C)

This instruction is similar to the READ UNIT "L" command; except (r) (r > 0) specifies

the number of logical records to be read instead of terminating input with a logical record

of zeros as in READ UNIT "L". The data within a block, which is not read, is lost.

CREAD UNIT "L" "L" format, using (n) blocks, into cols (C) ... (C)

Similar to the above instruction; except a FORMAT "L" is referenced and similar to the

READ UNIT "L" "L" instructions; except (n) (n > 0) blocks of data are read.

CSET UNIT "L", using (r) logical records, into column (C)

This instruction is similar to the SET UNIT instruction (see below). Here, the

indicated number of logical records (r) (r > 0) is read, whereas the SET UNIT command reads

data until a logical record of zeros is encountered. If all the data from a block is not

read, the rest of the information will be lost.

/

' “

7

/ CSET UNIT "L", using (r) logical records, into row (R) of column (C) /

/ /

46
Similar to above instruction; except data is entered into row (R) and below.

ENDFILE UNIT "L"

This instruction writes an end-of-file mark on the specified unit, "L". It is necessary

to ENDFILE a unit after all the data has been written on that unit. Otherwise, when the

data is read at some future time, extraneous information may be entered into the worksheet.

READ UNIT "L" into columns (C) , (C) ... (C)

This instruction is similar to the READ command; except data is read from the specified

unit rather than from cards. Reading of data continues until a logical record of zeros is

encountered (not a blank logical record) . The execution of this instruction always starts

at the beginning of a new block. Data for any particular row of all the columns specified

must be completely contained within a logical record. If all the data from a block is not

used, it is lost. Subsequent READ UNIT or CREAD UNIT instructions start with a new block.

READ UNIT "L” "L" format into columns (C) , (C) , ... (C)

Same as above; except unit is read using the specified format. Reading of data is under

format control and not under the control of OMNITAB. Data will be entered into the

specified columns until some row contains all zeros (i.e., a block of zeros). This

instruction always starts at the beginning of a new block for each row of data entered.

Data for any particular row may be in one or more blocks.

Warning : One must be aware of exactly how logical records are blocked and must set un the

format accordingly. Generally information should be read with the same format as it was

written.

REWIND UNIT ML

M

The unit specified by ”L" is positioned to the beginning of the unit.

SET UNIT ”LM into column (C)

This instruction is similar to a SET instruction; except data is read from the specified

unit rather than cards. Reading of data continues until a logical record of zeros is

encountered; not a blank logical record. Extra data, if any, in the block will be lost.

/

'

/ SET UNIT "L" starting with row (R) of column (C)

/

^

~7

/

/

Same as above; except first datum is entered into row (R) instead of row 1.

SKIP UNIT "L", forward (r) logical records

47
The specified unit "L" is moved forward (r) logical records. The instruction is just

like a BACKSPACE UNIT instruction; except the unit is moved forward instead of backwards.

The unit is positioned at the beginning of a block.

UNIT with (c) characters per logical record

The number of characters per logical record is set equal to (c) . The number of logical

records per block is assumed to be one.

/ UNIT with (c) characters/logical record and (r) logical records/block /

/. /

The number of characters per logical record is set equal to (c) and the number of

logical records per block is set equal to (r) . This instruction should be used with blocked

tapes or data. The instruction controls the number of characters per logical record and

number of logical records per block for all qualifiers. Thus, if two mediums are used with

differing values of (c) and (r) , a different UNIT instruction must be used before the use of

each medium.

Theoretically, c x r should be less than or equal to the size of the worksheet, 12500.

However, since FORTRAN input/output procedures are used, a systems programmer should be

consulted to determine how large c x r can be. For NBS users c x r < 132 and no check is

made to see that this number is not exceeded.

WRITE UNIT ”LM from columns (C) , (C) ... (C)

A WRITE UNIT instruction is to tapes, discs or drums what PUNCH is to cards. Data is

written on the unit "L" with c characters per logical record with r logical records per

block See the description of UNIT instruction for a definition of c and r. The default

values of c and r are 80 and 1 respectively. Each row of data starts a new logical record.

Let i = the number of columns specified in the instruction and j = integral part of c/15.

Then the number of logical records written for each row is the integral part of i/j + k,

where k = 0, if i/j has no remainder and k = 1, if i/j remainder is not zero. Each row

starts a new logical record. If the last logical record contains less than c characters, it

will be packed with blank characters. A row of zeros is written after all the rows of data.

If the last block has only m logical records (m < r) , then the last r - m logical

records will contain zeros. The instructions FIXED and FLOATING have no effect.

WRITE UNIT "L" "L" format, from columns (C) , (C) ... (C)

Same as above; except blocks are written according to the referenced format. The output

from this instruction is under format control and is not controlled by the OMNITAB II computing system. Each slash (/) in the format specification or each scan of the format

from the beginning will generate a new block. Furthermore, a row of zeros will be written

under format control after all the data is written.

Synonyms

BACKSPACE TAPE "L", CREAD TAPE "L" ENDFILE TAPE "L", READ TAPE "L",

SET TAPE "L", SKIP TAPE "L",

CREAD TAPE "L” "L", CSET TAPE "L",

READ TAPE "L" "L", REWIND TAPE "L",

WRITE TAPE "L", WRITE TAPE "L” "L"

48
2. ARITHMETIC OPERATIONS.

2.6+ Data Summarization .

AVERAGE, DAYS, MEDIAN, PERCENTAGES, PROPORTIONS, RANGE, STDDEV

/ 7

/ AVERAGE o£ numbers in columns (C) , (C) , . .. , (C) put in cols (C) , (C) , . . . , (C) /

/ _/

The optional form of AVERAGE is a generalization of the form described on the top of

page 94 of NBS TN 552 which makes it easy to compute the average of more than one column.

DAYS for month (E) day (E) and year (E) put in column (C)

The instruction DAYS converts a date like 8/5/76 into a unique number. For individual

dates, this number is of minor importance, unless one is coding data for a statistical

analysis. However, it can be very useful for finding the number of days between any two

dates. The instruction automatically adjusts for the differing numbers of days in the

months and years. The definition of a leap year is somewhat complicated and not widely

known, but the DAYS instruction uses it appropriately.

For the years in the 20th century (1900 to 1999) , it is not necessary to include the

first two digits of the year. Thus, 76 may be used instead of 1976.

To compute the number of days between January 17, 1976 and May 8, 1976, inclusive, we

could use the instructions

READ date into columns 1, 2 and 3 1/17/76

5/ 8/76

DAYS for columns 1,2, and 3 put in col 4 PROMOTE 1 row column 4 into col 5 SUBTRACT col 4 from col 5 and put in col 6 ADD 1.0 to column 6 and put in column 6 ABRIDGE row 1 of col 6

or

READ date into columns 1, 2 and 3 1/16/76

5/ 8/76

DAYS for columns 1,2, and 3 put in col 4 PROMOTE 1 row column 4 into col 5 SUBTRACT col 4 from col 5 and put in col 6 ABRIDGE row 1 of col 6

to get the answer 112.

For reference purposes, the number computed for 1/1/100 is 1 and the number computed for

1/1/77 is 685,412. No month, day or year can be negative or zero. Because of the

49
convention described above, of being able to use only the last two digits for years between

1900 and 1999, inclusive, years between 1 AD and 99 AD cannot be used. Note, according to

American, but not European, tradition, the month comes before the day rather than after it.

MEDIAN of numbers in column (C) put in column (C)

If the numbers 3.1, 17.6, 11.6, 9.2 and 8.3 are in column 17, the instruction

MEDIAN of column 17 put in column 18

would put the median 9.2 into every row of column 18. The median is defined on page 120 of

NBS TN 552.

/ /

/ MEDIAN of columns (C) , (C) , . .. , (C) put in column (C) , (C) , . .. , (C) /

/ /

The optional form of the MEDIAN instruction makes it possible to compute the median of

several columns with a single instruction.

PERCENTAGES of numbers in column (C) put in column (C)

Each number in the first named column is divided by the sum of the numbers, multiplied

by 100.0 and put into the second column. If the numbers 8, 11, 14, 5 and 1 are in column 8,

the instruction

PERCENTAGES of column 8 put in column 23

would put the numbers 20.512820, 28.205128, 35.897435, 12.820513 and 2.5641026 in column 23.

/ /

/ PERCENTAGES of columns (C) , (C) , . .

. , (C) put in column (C) , (C) , . .

. , (C) /

/ /

The optional form of the PERCENTAGES instruction makes it possible to compute the

percentages of numbers in several columns.

: PROPORTIONS of numbers in column (C) put in column (C)

A PROPORTIONS instruction is similar to the PERCENTAGES instruction, described above;

except the numbers in the second column are not multiplied by 100. If the numbers 8, 11,

14, 5 and 1 are in column 36, the instruction

PROPORTIONS of column 36 put in column 52

would put the numbers 0.2051282, 0.28205128, 0.35897436, 0.12820513 and 0.025641026 in

column 52.

50

I
/ 7

/ PROPORTIONS of columns (C) , (C) , . .

. , (C) put in columns (C) , (C) , . .. , (C) /

/ /

The optional form of the PROPORTIONS instruction makes it possible to compute the

proportions of numbers in several columns.

RANGE of numbers in column (C) put in column (C)

The difference between largest and smallest numbers in the first column is put in the

second column. If the numbers 3.1, 17.6, 9.2, 11.6 and 8.3 are in column 31, the

instruction

RANGE of column 31 put in column 32

would put the number 14.5 in every row of column 32.

/

/ RANGE of columns (C) , (C) , . .

. , (C) put in columns (C) , (C) , . .

. , (C)

/

~7

/

/

The optional form of the RANGE instruction makes it possible to compute the range for

several columns of data.

STDDEV of numbers in column (C) put in column (C)

Compute the standard deviation, as defined on page 120 of NBS TN 552, of the numbers in

the first column. If the numbers (see Section B3.5 of NBS TN 552) 9000, 9001 and 9003 are

in column 6, the instruction

STDDEV of column 6 put in column 7

would put the number 1.5275252 in every row of column 7.

/

/ STDDEV of columns (C) , (C) , . .

. , (C) put in columns (C) , (C) , . .. , (C)

/ /

The optional form of the STDDEV instruction makes it possible to compute the standard

deviations of numbers in several columns.

51
3. DATA MANIPULATION.

3.5+ Manipulative Operations .

CENSOR EQ, CENSOR GE, CENSOR GT,

CENSOR LE, CENSOR LT, CENSOR NE

The following six instructions are modifications of the CENSOR instruction with no modifier

described on page 102 of "OMNITAB II User's Reference Manual."

/ 7

/ CENSOR EQ col (C) for values equal to (E) , replace by (E) , put in col (C) /

/ /

/ /

/ CENSOR GE col (C) for nos greater than or eq. to (E) , replace by (E) , put in (C) /

/ /

/ /

/ CENSOR GT col (C) for values greater than (E) , replace by (E) , put in col (C) /

/ /

/ /

/ CENSOR LE col (C) for nos less than or equal to (E) , replace by (E) , put in (C) /

/ /

/ 7

/ CENSOR LT col (C) for values less than (E) , replace by (E) , put in col (C) /

/ 7

/ i

/ CENSOR NE col (C) for values not equal to (E) , replace by (E) , put in col (C) /

/ /

A CENSOR LE instruction is synonymous with a CENSOR instruction with no modifier.

For the numbers

the instructions

column 11: 2.3,

column 12: 3.4,

7.6, 5.2, 8.3, 4.2

5.8, 5.2, 4.7, 6.3

CENSOR EQ col 11 for 7.6

CENSOR GE col 11 for col 12 CENFOR or col 11 for 4.5

GET R

T T? col 11 for col 12 CENbOh I.T col 11 for 6.0

replace by 0.0, put in col

replace by -1.0, put in col

replace by col 12, put in col

replace by -1.0, put in col

replace by col 12, put in col

21

22

23

24

25

52
CENSOR NE col 11 for col 12, replace by -1.0, put in col 26

would leave columns 11 and 12 unchanged and put the following numbers in columns 21 to 26:

21 22_ 23 24 25 26

2.3 2.3 2.3 -1.0 3.4 -1.0

0.0 -1.0 5.8 7.6 7.6 -1.0

5.2 -1.0 5.2 -1.0 5.2 5.2

8.3 -1.0 4.7 8.3 8.3 -1.0

4.2 4.2 4.2 -1.0 6.3 -1.0

3.5,1 Editing Data .

CHOOSE, CODE, DELETE, OMIT,

RECODE, REPLACE, RETAIN

The instructions described in this section are useful for editing data or for data

manipulation in the following problem areas.

1. Deletion . Certain data are to be deleted for any one of a number of reasons:

(a) coded missing observations, (b) outliers, (c) possible outliers, (d)

analysis of subsets of data, etc.

2. Retention . This is the complement of deletion above and is done for similar

reasons . A person's height equal to -10cm should be deleted as an outlier. At the same time, we would want to retain all positive heights, knowing that all negative

heights are outliers by definition.

3. Coding . There are a number of reasons for coding data. For example, to

(a) specify subsets for further analysis, (b) simplify data, (c) aggregate

values to be treated alike, e.g., all persons aged 10 to 14, (d) identify missing

observations, etc.

Missing observations have to be identified. A blank is unsatisfactory. Sometimes zero can be used, but at other times it cannot be used. Missing observations must be coded and

subsequently deleted from further analysis. The coded value must be outside the known range

of permissible values.

The instructions OMIT and DELETE are useful for deleting data, CHOOSE and RETAIN for

retaining data, and CODE, RECODE, and REPLACE for coding data. DELETE is similar to OMIT;

except it can operate on several columns simultaneously. Also, RETAIN is similar to CHOOSE;

except it can operate on several columns simultaneously.

The CHOOSE, DELETE, OUT, and RETAIN instructions all reset NRMAX. The following

informative diagnostic message (see Section B1.6) is always printed:

NRMAX HAS BEEN RESET FROM (m) TO (n)

where (m) is the value of NRMAX before the instruction is executed and (n) is the value of

NRMAX after the instruction is executed.

Caution . Because of rounding, truncation, or conversion errors, a number in the

computer may be slightly different from what we think it should be. For example, a number

believed to be 8.5 may actually be something like 8.4999999 or 8.5000001. If an instruction

is looking for numbers equal to 8.5, it may not succeed when it should. This is not a

53
serious problem. However, when it does (or might) occur, a ROUND (n) , (C) , (C) instruction

can be used to avoid any difficulty.

CHOOSE rows with (K) in column (C) and put in column (C)

All numbers in the first column equal to (K) are stored in the second column. Zeros are

put at the bottom of the second column. NRMAX is reset to the number of rows in the first

column which have the value (K)

.

/ /

/ CHOOSE rows with numbers between (K) and (K) in column (C) and put in col (C) /

/ /

All numbers in the first column between (K) and (K) , inclusive, are stored in the second

column. Zeros are put at the bottom of the second column and NRMAX is reset accordingly.

/ /

/ CHOOSE rows with (K) in (C) , corresp . rows of (C) , . .

. , (C) put in (C) , . .. , (C) /

/ /

The number of arguments in this form of CHOOSE is 1 + 2n, where n = 1, or 2, or 3, ..., up to 49. There are two sets of n column numbers. If (K) is found in any row of the first

column of the first set of columns, that row of every column in the first set is included in

the second set of columns. The other rows are omitted. Zeros are put at the bottom of the

second set of columns and NRMAX is reset.

/ 7

/ CHOOSE from (K) to (K) in (C) , corresp . rows of (C) , . .

. , (C) put in (C) , . .

. , (C) /

/ /

This fourth form of CHOOSE bears the same relation to the second form as the third form

does to the first. The instruction has 2 + 2n arguments, where n can be any number from 1 to 48. For the data in columns 1 to 4 shown below, the instructions

CHOOSE rows with 6.0 in column 1 and put in column 5

RE,SET 15 CHOOSE rows with numbers between 5.0 and 8.0 in column 1 and put in column 6 RESET 15

CHOOSE rows with 6.0 in col 1 corr row of col 2 put in cols 7 and 8 RESET 15

CHOOSE rows between 5.0 and 8.0 in col 1 corr rows of col 2 put in cols 9 and 10

would put data in columns 5 to 10 as shown in the following table:

54
Row/ Column 1 2 3 4 5 6 7 8 9 10

1 6 25 40 101 6 6 6 25 6 25

2 7 23 48 -1 6 7 6 25 7 23

3 7 25 40 104 6 7 6 -3 7 25

4 6 25 49 109 6 6 6 25 6 25

5 -2 26 40 106 6 7 6 23 7 24

6 7 24 48 102 6 6 6 25 6 -3

7 6 -3 40 103 6 6 25

8 6 25 49 108 6 6 23

9 6 23 -2 105 7 7 24

10 7 24 48 108 6 6 25 11 -1 24 48 102 7 7 26

12 6 25 49 101 7 7 25 13 7 26 48 -1 7 7 26

14 7 25 48 103 0 0 0 15 7 26 49 107 0 0 0

For the first CHOOSE instruction, the number 6 occurs in the six rows 1, 4, 7, 8, 9, and 12 of column 1. The numbers in the other rows of column 1 are not put into column 5. NRMAX is reset to 6 = 15-9. For the second CHOOSE instruction, all the numbers in column 1 are between 5 and 8; except the numbers in rows 5 and 11. The numbers in rows 5 and 11 of

column 1 are not put into column 6. NRMAX is reset to 13 = 15 - 2. The third CHOOSE

instruction behaves just like the first and in addition for each row that has a six in

column 1 the corresponding row of column 2 is put into column 8. NKMAX is reset to 6 = 15 - 9. The fourth CHOOSE instruction behaves just like the second, but whenever a row from

column 1 is put into column 9 the same row of column 2 is put into column 10.

CODE column (C) using length (K) put in column (C)

The CODE instruction divides the numbers in the first column into intervals of length

(K) ; starting with the smallest number in the column. All numbers in the first interval are assigned the value 1, all numbers in the second interval are assigned the value 2, etc. and

the results are put in the column designated by the third argument. The instruction is

useful for aggregating values to be treated alike. For example, all persons aged 10 to 14

could be put into one class. If the numbers 12.6, 36.4, 27.1, 6.2 and 3.7 are in column 1,

the instruction

CODE column 1 using length 5.0 put in column 4

would put the numbers 2, 7, 5, 1, and 1 into column 4.

/

' “ '

' 7

/ CODE column (C) starting at (K) using length (K) put in column (C) /

/

^ /

This form of the CODE instruction is the same as the one above; except the starting

value, indicated by the second argument, is specified instead of being assigned

automatically the minimum value in the column. All numbers in the first column which are

less than the specified starting value are assigned the coded value 0.0.

For the numbers 12.6, 36.4, 27.1, 6.2, and 3.7 in column 1, the instruction

CODE column 1 starting at 0.0 using length 5.0 put in column 2

would put the numbers 3, 8, 6, 2, and 1 into column 2. On the other hand, the instruction

CODE column 1, starting at 10.0, using length 5.0, put in column 3

55
would put the numbers 1, 6, 4, 0, and 0 into column 3. In the last instruction, the

starting value 10.0 exceeds the minimum value 3.7. The values 6.2 and 3.7, which are below

the starting value, are coded 0.0.

DELETE rows having (K) in rows of any cols (C) , . .

. , (C) put in cols (C) , . .

. , (C)

If the number (K) is in a row of any column in the first set of columns, that row is not

put into the second set of columns. The other rows in the first set of columns are retained

in the second set of columns. Zeros are put at the bottom of the second set of columns and

NRMAX is reset to the number of rows retained. The instruction

DELETE all rows having -1.0 in any of cols 1,2,3, and 4 and put in cols 11,12,13, and 14

would produce the following results for the numbers shown in columns 1 to 4:

Row/Column 1 2 3 4 11 12_ 13 14

1 6 25 40 101 6 25 40 101

2 7 23 48 -1 7 25 40 104

3 7 25 40 104 6 25 49 109

4 6 25 49 109 -2 26 40 106

5

- ?L 26 40 106 7 24 48 102

6 1 24 48 102 6 -3 40 103

7 6 -3 40 103 6 25 49 108

8 6 25 49 108 6 23 u 105

9 6 23 -2 105 7 24 48 108

10 7 24 48 108 6 25 49 101

11 -1 24 48 102 7 25 48 103

12 6 25 49 101 7 26 49 107

13 7 26 48 -1 14 7 25 48 103

15 7 26 49 107

The number -1 occurs in row 2 of column 4, row 11 of column 1, and row 13 of column 4. Hence, rows 2, 11, and 13 are deleted when the results are stored in columns 11 to 14.

NRMAX is reset to 15 - 3 = 12.

/ 7

/ DELETE rows itfith numbers between (K) and (K) in (C) , . .

. , (C) put in (C) , . . . , (C) /

/ /

All rows in the first set of columns with a number between (K) and (K) , inclusive, are

not included in the second set of columns. The other rows in the first set of columns are retained in the second set of columns. Zeros are put at the bottom of the second set of

columns and NRMAX is reset to the number of rows retained. The instruction

DELETE all numbers between -10.0 and 0.0 in cols 1,2, 3, and 4 put in cols 15,16,17, and 18

would produce the following results for the numbers shown in columns 1 to 4:

56
Row/Column 1 2 3 4 15_ 16 17 18

1 6 25 40 101 6 25 40 101

2 7 23 48 -1 7 25 40 104

3 7 25 40 104 6 25 49 109

4 6 25 49 109 7 24 48 102

5 -2 26 40 106 6 25 49 108

6 7 24 48 102 7 24 48 108

7 6 -3 40 103 6 25 49 101

8 6 25 49 108 7 25 48 103

9 6 23 -2 105 7 26 49 107

10 7 24 48 108

11 -1 24 48 102

12 6 25 49 101

13 7 26 48 -1

14 7 25 48 103

15 7 26 49 107

In this example, the six rows 2, 5, 7, 9, 11, and 13 contain numbers in columns 1 to 4 which

are in the range -10.0 to 0.0 and are deleted. All the nunibers in the remaining rows of

columns 1 to 4 are outside the range -10.0 to 0.0 and these rows are retained. NRMAX is

reset to 15 - 6 = 9.

OIIT rows with (K) in column (C) and put in column (C)

If a number in the first column is not equal to (K) , it is put in the second column. If

a number in the first column is equal to (K) , zero is put at the bottom of the second

column. NRMAX is reset to the number of rows retained in the second column.

/ /

/ OUT rows with numbers between (K) and (K) in column (C) and put in column (C) /

/ /

If a number in the first column is not between (K) and (K) , inclusive, it is put in the

second column. If a number in the first column is in the interval (K) to (K) , zero is put

at the bottom of the second column. NRMAX is reset to the number of rows retained in the

second column.

/ /

/ OIIT rows with (K) in col (C) , corresp . rows of (C) , . .

. , (C) put in (C) , . .

. , (C) /

/ /

The number of arguments in this form of OMIT is 1 + 2n, where n = 1, or 2, or 3, . .

.

,

up

to 49. There are two sets of n column numbers. If (K) is found in any row of the first

column of the first set of columns, that row of every column in the first set is not

included in the second set of columns . Zeros are put at the bottom of the second set of

columns and NRMAX is reset.

/ / / OUT from (K) to (K) in (C) , corresp . rows of (C) , . .

. , (C) put in (C) , . . .

,

(C) /

/ /

This fourth form of OUT bears the same relation to the second form as the the third

form does to the first. The instruction has 2 + 2n arguments, where n can be any number

from 1 to 48.

For the data in columns 1 to 4 shown below, the instructions

57
OMIT rows with -2.0 in column 1 and put in column 5 RESET 15 OMIT rows with numbers between -10.0 and 0.0 in column 1 and put in column 6

RESET 15

OlIT rows with -2.0 in col 1 corr row of col 2 put in cols 7 and 8 RESET 15

OIIT rows between -10.0 and 0.0 in col 1 corr rows of col 2 put in cols 9 and 10

would put data in columns 5 to 10 as shown in the following table:

Row/Column 1 2 3 4 5 6 7

_ 8 9 10

1 6 25 40 101 6 6 6 25 6 25

2 7 23 48 -1 7 7 7 23 7 23

3 7 25 40 104 7 7 7 25 7 25

4 6 25 49 109 6 6 6 25 6 25

5 -2 26 40 106 7 7 7 24 7 24

6 7 24 48 102 6 6 6 -3 6 -3

7 6 -3 40 103 6 6 6 25 6 25

8 6 25 49 108 6 6 6 23 6 23

9 6 23 -2 105 7 7 7 24 7 24

10 7 24 48 108 -1 6 -1 24 6 25

11 -1 24 48 102 6 7 6 25 7 26

12 6 25 49 101 7 7 7 26 7 25

13 7 26 48 -1 7 7 7 25 7 26

14 7 25 48 103 7 7 26

15 7 26 49 107

For the first OIIT instruction, there is a -2 in row 5 of column 1 and this row is omitted

in column 5. NRMAX is reset to 15 - 1 = 14. For the second OMIT instruction, the numbers

in rows 5 and 11 in column 1 are between -10.0 and 0.0 and these rows are omitted in column

6. NRMAX is reset to 15 - 2 = 13. For the third OIIT instruction, there is a -2 in row 5 of column 1 and hence row 5 is omitted from columns 7 and 8. NRMAX is reset to 15 -1 = 14.

For the fourth (and last) OUT instruction, the numbers in rows 5 and 11 in column 1 are between -10.0 and 0.0 and hence rows 5 and 11 of columns 1 and 2 are omitted in columns 9

and 10. NRMAX is reset to 15 - 2 = 13.

RECODE column (C) into column (C)

The number 1 is assigned to the smallest number in the first column, the number 2 to the

second smallest number, and so on and the result is stored in the column designated by the

second argument. If the numbers 40, 49, 48, 40, 49, and 48 are in column 2, the instruction

RECODE column 2 into column 5

would put the numbers 1 , 3 , 2 , 1 , 3 , and 2 into column 5

.

REPLACE the number (K) in column (C) by the number (K) and put in column (C)

The replace instruction is another coding instruction which simply replaces one number

by another. It can be used for the treatment of missing or outlying data. For the numbers

-1, -9, 2, 5, and -9 in column 5, the instruction

REPLACE -9.0 in column 5 by 3.0 and put in column 7

would put the numbers -1, 3, 2, 5, and 3 into column 7.

58
/

' / / REPLACE the numbers from (K) to (K) in col (C) by (K) and put in column (C) /

/ /

This form of REPLACE is the same as the one above; except a range of numbers, instead of

just one, is replaced by a single number. For the numbers -1, -9, 2, 5, and -9 in column 5, the instruction

REPLACE numbers from -3.0 to +3.0 in col 5 by 7.0 and nut in col 9

would put the numbers 7, -9, 7, 5, and -9 into column 9.

RETAIN numbers between (K) and (K) in cols (C) , . .

. , (C) nut in cols (C) , . .

. , (C)

Any row in the first set of columns with all numbers between (K) and QO , inclusive, is put at the top of the second set of columns. Any row in the first set of columns which has

a number outside the range (K) to (K) is deleted. Zeros are put at the bottom of the second

set of columns and NRMAX is reset to the number of rows retained. The instruction

RETAIN all numbers between 0,.0 and 200.0 in cols 1,2, 3, and 4 put in cols 11,12

would produce the following results for the numbers shown in columns 1 to 4 :

Row/ Column 1 2 3 4 11 12 13 14

1 6 25 40 101 6 25 40 101

2 7 23 48 -1 7 25 40 104

3 7 25 40 104 6 25 49 109

4 6 25 49 109 7 24 48 102

5 -2 26 40 106 6 25 49 108

6 7 24 48 102 7 24 48 108

7 6 -3 40 103 6 25 49 101

8 6 25 49 108 7 25 48 103

9 6 23 -2 105 7 26 49 107

10 7 24 48 108

11 -1 24 48 102

12 6 25 49 101

13 7 26 -1 -1

14 7 25 48 103

15 7 26 49 107

In this example, the six rows 2, 5, 7, 9, 11, and 13 contain numbers in columns 1 to 4 which

are outside the range 0.0 to 200.0 and are deleted. All the numbers in the remaining rows

of columns 1 to 4 are within the range 0.0 to 200.0 and these rows are retained. NRMAX is

reset to 6 = 15 - 9.

59
4. STATISTICAL ANALYSIS.

4.1* Elementary Analysis .

If the specified starting point (K) of a FREQUENCY distribution instruction exceeded the

smallest number in the data column, trouble developed. A correction has been made so that

data below the starting point are ignored. If the numbers 1.0 to 10.0 are in column 2, the

instruction

:

FREQUENCY of col 2, using 3 classes of length 2.0, starting at 4.5, put in col 7

will now store the frequency 2.0 in the first three rows of column 7 for the three classes:

4.5 to 6.5

6 . 5 to 8.5

8.5 to 10.5

Note, that the four measurements 1.0, 2.0, 3.0 and 4.0 are simply ignored and the total

frequency count is 6 rather than 10. Furthermore, no diagnostic is given. One can

deliberately set the starting point above the minimum to advantage, but care should be

exercised in doing it.

Also, a similar situation exists if the specified number of classes (k) is not large

enough to include all the data. For the above example, the instruction:

FREQUENCY of col 2, using 3 classes of length 2.0, starting at 0.5, put in col 8

will now store the frequency 2.0 in the first three rows of column 8 for the three classes:

0.5 to 2.5

2.5 to 4.5

4.5 to 6.5

The total frequency count is six rather than ten, and the numbers 7.0, 8.0, 9.0 and 10.0 are

ignored. Again, no diagnostic is given.

If the number of classes exceeds the number of rows in the worksheet, the following

informative diagnostic is printed:

COLUMN NOT LONG ENOUGH TO STORE ALL NUMBERS.

FIRST (n) NUMBERS WERE STORED.

The HISTOGRAM and NIIISTOGRAM instructions have been revised to allow printing on narrow

carriage terminals or CRT devices. In addition, a scale is now printed below the column

titles and three columns have been added to show the relative cumulative frequencies,

cumulative frequencies and relative frequencies. Page 61 is the equivalent of page 114 of

NBS TN 552. Page 62 shows the result of using the instructions:

WIDTH 72 FREQUENCY of COMBINED MPG using 11 cells, length 2.0, start 11.5 store 11 12 13

HISTOGRAM 11, 12, 13

with the data on page 110.

60
OMNITAB 3/24/77 HISTOGRAM EXAMPLE PAGE 1

HISTOGRAM WITH MID-POINTS IN COLUMN 44 , FREQUENCIES IN COLUMN 43

MID-POINTS RCF CF RF FREQUENCY

0 10

-.25000000 .205 8 .205 8 ++++++++

.25000000 .487 19 .282 11 +++++++++++

.75000000 .846 33 .359 14 ++++++++++++++

1.2500000 .974 38 .128 5 +++++ 1.7500000 1.000 39 .026 1 +

RCF = RELATIVE CUMULATIVE FREQUENCY

CF = CUMULATIVE FREQUENCY

RF = RELATIVE FREQUENCY

0 io 20

-.25000000 .205 8 .205 8 ++++++++

.25000000 .487 19 .282 11 +++++++++++

.75000000 .846 33 .359 14 ++++++++++++++

1.2500000 .974 38 .128 5 +++++

1.7500000 1.000 39 .026 1 +

RCF = RELATIVE. CUMULATIVE FREQUENCY

CF = CUMULATIVE FREQUENCY

RE = RELATIVE FREQUENCY

OMNITAB 3/24/77 HISTOGRAM EXAMPLE PAGE 2

LIST OF DATA, INSTRUCTIONS AND DIAGNOSTICS

SET VELOCITY OF LIGHT IN COLUMN 1 4 .6 1.0 1.0 1.0 .5 .6 .7 1.0 .6 .2 1.9 .2 4 .0 -.4 -.3 .0 -.4 -.3 .1 -.1 .2 -.5 .3 -.1

2 -.2 .8 .5 .6 .8 .7 .7 .2 .5 .7 .8 1.1

* INFORMATIVE DIAGNOSTIC FOR THE ABOVE INSTRUCTION - NRMAX HAS BEEN RESET FROM 0 TO 39.

FREQUENCY OF COL 1 USING 5 CELLS OF LENGTH .5 START AT -.5 PUT IN COLS 41,42,43

* INFORMATIVE DIAGNOSTIC FOR THE ABOVE INSTRUCTION - NRMAX HAS BEEN RESET FROM 39 TO 5.

ADD COL 41 TO COL 42, MULT BY 0.5, ADD 0 .

0

AND PUT IN COL 44

HISTOGRAM WITH MID-POINTS IN COL 44 AND FREQUENCIES IN COL 43

SPACE 2 NHISTOGRAM WITH MID-POINTS IN COL 44 AND FREQUENCIES IN COL 43

6
OMNITAB 1/12/77 HISTOGRAM WITH JJF EPA76 DATA PG 1

HISTOGRAM WITH FREQUENCIES IN COLUMN 13

MID-POINTS RCF CF RF FREQUENCY

0 30 60 90

12.500000 .105 40 .105 40 ++++++++++++++

14.500000 .321 122 .216 82 ++++++++++++++++++++++++++++

16.500000 .455 173 .134 51 +++++++++++++++++

18.500000 .579 220 .124 47 ++++++++++++++++

20.500000 .721 274 .142 54 ++++++++++++++++++

22.500000 .803 305 .082 31 +++++++++++

24.500000 .868 330 .066 25 +++++++++

26.500000 .934 355 .066 25 +++++++++

28.500000 .982 373 .047 18 ++++++

30.500000 .987 375 .005 2 + 32.500000 1.000 380 .013 5 ++

RCF = RELATIVE CUMULATIVE FREQUENCY

CF = CUMULATIVE FREQUENCY

RF = RELATIVE FREQUENCY

62
Previously, a plus sign (+) was printed for each measurement. If there was not enough room to print all the plus signs for a given class on one line, the printing continued on additional lines. Now, printing for any class of the distribution is on a single line and

the scale is adjusted appropriately. Each plus sign may represent more than one measurement

as indicated by the scale at the top. Hie zero of the number printed in the scale is

printed at the Oth, 10th, 20th, etc. marks on the scale. In the second example discussed

above, each tenth mark on the scale is 0, 30, 60 and 90. Hence, each plus represents three

measurements; except the last plus may represent only one or two measurements.

Optional forms of HISTOGRAM and NHISTOGRAM now exist with three arguments instead of

two, which specify the class boundaries of the frequency distribution instead of the midpoints. However, the mid-points are still printed rather than the class boundaries.

/ 7

/ HISTOGRAM for class boundaries in columns (C) and (C) frequencies in column (C) /

/ /

/ /

/ NHISTOGRAM for class boundaries in columns (C) and (C) frequencies in column (C) /

/ /

4.1.1 Stem-and-Leaf Displays .

STEM LEAF, SSTEM LEAF

A STEM LEAF instruction produces a stem-and-leaf graphical display of data; an innovation of Tukey (1970) . The display is a concise representation of a set of

measurements which is similar to a histogram, but has certain advantages. The stem-and-leaf

display preserves more of the fine structure of the data and obviates the problem of

choosing an appropriate class width for the histogram. Additional information is given on

order statistics, such as the median, which makes it a better technique for studying the

frequency distribution of measurement data in order to formulate mathematical models and to

evaluate "outlying” values and other anomalies, so that summary measures (average, standard

deviation) can be correctly interpreted.

Very simply, a stem-and-leaf display replaces the mark (X) of a histogram by a digit,

thereby preserving the graphical features of the histogram and at the same time providing

more information. The three measurements 103, 107 and 108 might appear in a histogram as

100-109 XXX

The same three measurements could appear in a stem-and-leaf display as

10 | 378

The stem, 10, gives the first two digits of the measurements and the leaves 3, 7 and 8 give

the third digit. The technique has been generalized in a number of ways to provide displays

for all kinds of data.

There is a main instruction which uses an automatic rule to produce three, or sometimes

two, displays without any storage. Three, rather than one, displays are given partly

because it is not possible to automatically determine the best display and partly to allow

one to look at the data in different ways. Two options enable certain storage. The

automatic rule is good, but not infallible and sometimes does not produce the best display.

Also, there are times when one wants a specific display not produced by the automatic rule.

Hence, there are three other options to allow one to override the automatic rule and store

results. The four forms SSTEM LEAF suppress the automatic display.

63
The main instruction is simply

STEM LEAF of column (C)

Suppose column 7 contains the 114 measurements

:

116. 82. 127. 100. 94. 117. 90. 121. 93. 120. 83. 95. 88. 123.

81. 75. 100. 98. 88. 86. 80. 105. 117. 92. 81. 85. 90. 100.

106. 98. 93. 96. 90. 108. 138. 133. 83. 100. 85. 95. 98. 123.

109. 108. 119. 100. 101. 129. 80. 92. 119. 94. 104. 91. 96. 103.

131. 115. 104. 103. 116. 103. 133. 77. 65. 80. 134. 103. 104. 86.

81. 89. 115. 130. 149. 126. 85. 93. 126. 93. 93. 82. 95. 89. 83. 124. 95. 84. 82. 75. 97. 100. 83. 83. 77. 78. 125. 113.

104. 121. 105. 80. 92. 103. 116. 68. 86. 76. 102. 94. 141. 86.

126. 119.

The instruction

STEM LEAF of column 7

will cause the three displays shown on pages 65 and 66 to be printed. An explanation of a display follows.

Note, a careful examination of the stem-and-leaf display shows that some digits seem to occur more frequently than expected and some less frequently. In the third line of the

first display, there are several 0's, 3's and 6's, but only one 4 and no 7's. Further

investigation would be needed to determine whether the differences in expected frequencies

are real or simply due to chance, but in any case a histogram would not reveal any

difference

.

Automatic Display

Each display starts on a new page which begins with a title

STEM AND LEAF DISPLAY FOR COLUMN XX

where XX is a column number heading or label. The display has four columns for the (i)

depth, (ii) scrawl, (iii) stem and (iv) leaves. At the bottom of the display, additional

information is printed: (v) numerical values for the scrawl, (vi) units of measurement for

the stem and leaves with examples, and (vii) values of the four parameters used to determine

the form of the display.

64
STEM AND LEAF DISPLAY FOR COLUMN 7

:pth SCRAWL STEM LEAF

2 * 6 ( 58

8 7 ) 556778

35 H 8 ( 000011122233333455566668899

M 9 ) 0001222333334445555667888

54 10 ( 00000012333334444556889

31 H 11 ) 35566677999

20 12 ( 011334566679

8 13 ) 013348

2

* 14 ( 19

SCRAWL (N= 114, *= 65.000000, H= 86.000000,

M= 97.500000, H= 115.00000, *= 149.00000)

EACH STEM UNIT IS 10

EACH LEAF UNIT IS 1

6 ( 5 READS AS 65

14 ( 9 READS AS 149

FOR THE ABOVE STEM AND LEAF DISPLAY, THE FOLLOWING VARIABLES WERE USED

1= 1, J = 2, K = 2, L = 1

STEM AND LEAF DISPLAY FOR COLUMN 7

;PTH SCRAWL STEM LEAF

2 * 6 ( 58

2 7 ) 8 7 ( 556778

24 8 ) 0000111222333354

35 H 8 ( 55566668899

50 9 ) 000122233333444

M 9 ( 5555667888

54 10 ) 00000012333334444

37 10 ( 556889

31 11 ) 3 30 H 11 ( 5566677999

20 12 ) 011334

14 12 ( 566679

8 13 ) 01334

3 13 ( 8

2 14 ) 1

1 * 14 ( 9

SCRAWL (N= 114, *= 65.000000, H= 86.000000,

M= 97.500000, H= 115.00000, *= 149.00000)

EACH STEM UNIT IS 10

EACH LEAF UNIT IS 1 6 ( 5 READS AS 65

14 ( 9 READS AS 149

FOR THE ABOVE STEM AND LEAF DISPLAY, THE FOLLOWING VARIABLES WERE USED

1= 1, J = 2 , K = 4 , L = 1

65
STEM AND LEAF DISPLAY FOR COLUMN 7

DEPTH SCRAWL STEM LEAF

1 * 6 c 5

1 6 ) 2 6 ( 8

2 7 ) 2 7 ( 4 7 ) 55

7 7 ( 677

8 7 ) 8 15 8 ( 0000111

23 8 ) 22233333

27 8 ( 4555

31 H 8 ) 6666

35 8 ( 8899

39 9 ) 0001

47 9 ( 22233333

54 9 ) 4445555

57 9 ( 667

M 9 ) 888

54 10 ( 0000001

47 10 ) 233333

41 10 ( 444455

35 10 ) 6 34 10 ( 889

31 11 ) 31 11 ( 3 30 H 11 ) 55

28 11 ( 66677

23 11 ) 999

20 12 ( Oil

17 12 ) 33

15 12 ( 45

13 12 ) 6667

9 12 ( 9

8 13 ) 01

6 13 ( 33

4 13 ) 4

3 13 ( 3 13 ) 8

2 14 ( 1

1 14 ) 1 14 ( 1 14 ) 1

* 14 ( 9

SCRAWL (N= 114, *= 65.000000, H= 86.000000,

M= 97.500000, H= 115.00000, *= 149.00000)

EACH STEM UNIT IS 10

EACH LEAF UNIT IS 1 6 ( 5 READS AS 65

14 ( 9 READS AS 149

FOR THE ABOVE STEM AND LEAF DISPLAY, THE FOLLOWING VARIABLES WERE USED

66
(i) Depth column: The depth is basically a cumulative frequency count, but the

counting works both from the minimum down to the middle and from the maximum up to the

middle of the display. From the minimum to the median (top half of the display) , the depth

for any stem is the total number of measurements less than or equal to the measurement

corresponding to the largest leaf. On the second line of the first display in the above

example, the stem is 7 and the largest leaf is 8. This represents the measurement 78. The

depth, 8, is the total number of measurements less than or equal to 78. From the maximum to the median (bottom half of display) , the depth for any stem is the total number of

measurements equal to or greater than the measurement corresponding to the smallest leaf.

On the seventh line of the first display in the above example, the stem is 12 and the

smallest leaf is 0. Thus, there are 20 measurements equal to or greater than 120. A value

for the depth is not given for the stem where the median lies. In some cases, it will take more than one line to print all the leaves for a particular stem. When this occurs, the

depth is printed on the last line for that particular stem.

Nemenyi et al. (1975), give an approximate 95 percent confidence interval for the

population median of a set of measurements which can be easily determined from the depth of

a stem-and-leaf display. Let a be the integral part of (n+l)/2 - /n, where n is the number

of measurements. In our example n = 114 and a is the integral part of 57.5 - 10.7 = 46.8 or

a = 46. Now find the measurements corresponding to the depth a = 46 to obtain the 95

percent confidence interval. Thus, we can be 95 percent confident the median of our distribution is between 93 and 102.

(ii) Scrawl column : The five symbols used are:

* for the minimum, or smallest measurement

II for the lower hinge, or lower quartile

M for the median

H for the upper hinge, or upper quartile

* for the maximum, or largest measurement

The lower hinge is that value such that 25 percent of the measurements fall below it and

75 percent of the measurements fall above it. Similarly, the upper hinge is that value such

that 75 percent of the measurements fall below it and 25 percent of the measurements fall

above it. The median, as usual, is the value for which 50 percent of the measurements fall

below it and 50 percent of the measurements fall above it.

More precisely, let r = 1, 2 and 3 for the lower hinge, median and upper hinge and x(r)

be the measurements ordered from smallest to largest. Let m be the largest integer less

than or equal to m/4, where n is the number of measurements. If m/4 is not an integer use X(m + 1) to define the lower hinge, median and upper hinge. If r*n/4 is an integer, use

[X(m) + x(m + 1) ]/2.

Each symbol is printed on the line in which the appropriate leaf appears. In the above

example, the median is 97.5. For this measurement, the stem is 9 and the leaf 8. Thus, M

is printed on the fourth line of the first display opposite the 23nd leaf, 7.

(iii) Stem column : The stem gives the leading significant digits of a measurement.

In the first display of the above example, each stem is different (k = 2).

In the second display, two lines have the same stem (k = 4). The first is used for

leaves with values 0, 1, 2, 3 or 4 and the second for leaves with the values 5,6,7, 8 or

9. Thus, the six numbers in the 130 's, namely, 130, 131, 133, 133, 134 and 138, which are represented on one line in the first display are represented on two lines in the second

display.

In the third display, five lines have the same stem (k = 10). The first is used for

leaves with values 0 or 1, the second for leaves with values 2 or 3, the third for 4 or 5,

67
the fourth for 6 or 7 and the fifth for 8 or 9. Thus, five lines are used to represent the

six numbers in the 130 's.

If there is more than one stem on a line, the steins are separated by a slash (/) and the

corresponding sets of leaves are separated by a slash. Thus

4/5/6

could be used to display all numbers from 40 to 69 and

4/5/6 ( //3

would represent 63.

(iv) Leaf column: The leaves are separated from their stems by alternating left and

right parentheses . (A vertical line symbol, which would be preferable, is not available on

some computers.) The leaf is the next significant digit after the last significant digit in

the stem. If a leaf has more than one digit (see below) , the leaves are separated by a

comma. This is also true for mixed displays when any of the leading digits are actually

part of the stem (see below)

.

(v) Scrawl: Values of the smallest measurement, lower hinge, median, upper hinge and

largest measurement are printed after giving the total number of measurements. In the above

example, the smallest of the 114 measurements is 65 and the largest is 149. The lower hinge

is 86.0, the median is 97.5, and the upper hinge is 115.0.

(vi) Scale: Below the scrawl, the units of measurement for the stems and leaves are printed and examples are given for the smallest and largest measurements.

(vii) Parameters : The values of four parameters i, j, k and £ uniquely determine the

form of a display and are printed at the bottom of each display. The values can be set as

in three of the options described below.

Options .

The following two instructions are identical to the main STEM LEAF instruction, except

they allow one to store the scrawl (or the scrawl and the depth) in the worksheet.

/

' 7

/ STEM LEAF of column (C) and put scrawl in column (C) /

The scrawl will be stored in the second named column. The eight values stored are.

Row Description

123

4

5

6

7

8

NRMAX

minimum value of data

the next value after the minimum

the lower hinge

the median

the upper hinge

the value preceding the maximum

maximum value of the data

68
/

/ STEM LEAF of col (C) , scrawl in (C) and put depth in column (C)

/

/

/

Both the depth and the scrawl are stored in the specified columns. The first row of the

column, where the depth information is stored, contains the total number of stems. The

depth of the first stem line is in row 2, depth of the second stem line in row 3 and so

forth. If the number of stems is greater than the number of rows, only the number of rows minus one depths will be stored.

The next three instructions are similar to the three preceding ones, except the user

defines the parameters i , j , k and £

.

/ /

/ STEM LEAF of column (C) for parameter values (i) , (j), (k) and (£) /

/ J

/

"7

/ STEM LEAF of col (C) for (i) , (j), (k) and (£) put scrawl in col (C) /

/ /

/ /

/ STEM LEAF of col (C) for (i) , (j), (k) , (£) put scrawl in (C) and depth in (C) /

/ /

(i) : The parameter i determines whether a simple display is given, i >0, or a mixed

display is given, i = 0.

Mixed stem-and-leaf displays, which are necessarily more complicated, can be used to provide a more concise display when the data vary over several orders of magnitude.

The numbers 78, 85, 96, 102 and 113 can be easily shown with a simple stem-and-leaf

display as:

7 ( 8

8 ) 5 9 ( 6 10 ) 2 11 ( 3

The numbers 78, 85, 96, 198, 276 could not be displayed so easily without using a lot of

stems (10, 11,..., 18 and 20, 21,..., 26) which had no leaves. But a mixed stem-and-leaf

display would look like:

7 ( 8

8 ) 5 9 ( 6 1* ) 98

2* ( 76

The asterisk after the 1 (or 2) means that the first digit of the leaf is really part of the

stem. Thus, 1* ( 98 reads as 198, whereas 1 ( 98 reads as 19 and 18. If two asterisks are

used, then the first two digits of the leaf are actually part of the stem, etc.

(j) : The parameter j is the number of significant digits in the largest stem. It is

increased by one, if the decimal point falls within the stem. The value of j must be

69
. 

i greater than zero. In the first display of the original example, the smallest stem is 6 and

the largest is 14 with two digits and hence j = 2

.

(k) : The parameter k controls the length of each stem. If the display is simple (i >

0) , then k = 1, 2, 4 or 10. If k = 2, each stem is on a different line. If k = 4, a stem

will appear twice. If k = 10, a stem will appear five times. Thus, the numbers 61, 68 and

73 (or 610, 680 and 730) would be displayed as:

6 ( 18

7 ) 3 for k = 2, and as

6 ( 1 6 ) 8

7 ( 3 for k = 4, and as

6 ( 1 6 ) 6 ( 6 ) 6 ( 8

for k = 10. In other words, the unit of measurement is 1, .5 and .2 times some power of ten

depending upon whether k = 2, 4 or 10.

If k = 1, there will be two stems per line; each separated by a slash. The above three

numbers, 61, 68 and 73 could be displayed as:

6/7 ( 18/3

Note, that the leaves for each stem are separated by a slash.

If the display is mixed, i = 0, then k = 1 or 3. If k = 1, there will be three stems

per line as in:

6/7/8 (

If k = 3, each stem will be on a new line as in:

6 ( 18/3/

7 ) 3

8 (

(ft) : The value of the parameter ft determines the number of digits in any leaf. For the

instruction

STEM LEAF of col 7 for i=l, j=2, k=l, and ft=l

only one display form is printed. This display contains two stem values per stem line.

STEM AND LEAF DISPLAY FOR COLUMN 7

DEPTH SCRAWL STEM LEAF

8 * 6/ 7 ( 58/556778

HM 8/ 9 ) 000011122233333455566668899/0001222333334445555667888

54 H 10/11 ( 00000012333334444556889/35566677999

20 12/13 ) 011334566679/013348

2 * 14/15 ( 19/

70
SCRAWL (N= 114, *= 6S.000000, H= 86.000000,

M= 97.500000, H= 115.00000, *= 149.00000 ) EACH STEM UNIT IS 10

EACH LEAF UNIT IS 1 6/7 (5 READS AS 65

14/15 ( 9 READS AS 149

FOR THE ABOVE STEM AND LEAF DISPLAY, TIE FOLLOWING VARIABLES WERE USED

I = 1, J = 2, K = 1, L = 1

Suppress Display .

The four forms of the SSTEM LEAF instruction are the same as the second, third, fifth

and sixth forms of the STEM LEAF instruction except the automatic display is suppressed.

SSTEM LEAF of column (C) put scrawl in column (C)

/ / / SSTEM LEAF of col (C) put scrawl in col (C) and depth in col (C) /

/ /

/ /

/ SSTEM LEAF col (C) for (i) , (j), (k) and (2.) , put scrawl in col (C) /

/ /

/ /

/ SSTEM LEAF of (C) for (i) , (j), (k) and (£) , put scrawl in (C) and depth in (C) /

/ /

Diagnostics :

Three fatal errors may occur in the use of the STEM LEAF or SSTEM LEAF instructions. If

NRMAX is less than 5 or all the data values are the same, the following fatal error will be

given :

NRMAX IS LESS THAN 5 OR ALL MEASUREMENTS ARE THE SALE.

Whenever the range of data is too large, the fatal error

VALUE OF SOME MEASUREMENT IS NOT ACCEPTABLE.

is printed. The most frequent fatal error occurs if any of the parameter values i, j, k

and/or l are not defined correctly, or if the rules for determining the parameters are not

satisfied. The error message given for these difficulties is:

I, J, K AND/OR L NOT DEFINED CORRECTLY, OR RULE

CANNOT DETERMINE PROPER PARAMETER VALUES FOR THESE DATA.

71
The informative diagnostics which can occur with STEM LEAF and SSTEM LEAF instructions

are:

PRINTING OF STEM AND LEAF DISPLAY IS NOT POSSIBLE.

INSTRUCTION WAS TREATED AS SSTEM LEAF.

This happens if the stem (j) is very large and the defined printer line width is small.

(n) ROWS IN WORKSHEET IS NOT ENOUGH FOR COMPLETE STORAGE.

and

DISPLAY IS (n) LINES, ONLY FIRST 99 DISPLAYED.

If the parameters i, j, k and £ are not defined carefully, the total number of stems may be

huge.

Rule .

If the parameters are not defined by the user, STEM LEAF and SSTEM LEAF instructions

will set i and £ equal to one and determine the best pair of j , k by one of the following

rules

.

Primary Rule.

Let A = number of display lines from lower hinge to upper hinge divided by the total

number of lines in display.

Let B = total number of lines in display divided by the number of measurements.

Then choose j and k such that .2 <_ A < .5, B <_ .15 and k > 2. Otherwise stop whenever

(i) j + £ + 1 is greater than the number of significant digits Th the data; (ii) B ^ .03; or

(iii) total number of display lines is greater than 50(logion).

Secondary Rule.

If the primary rule is not satisfied, choose the first pair of j , k such that k is

greater than one and the total number of display lines is greater than or equal to 30.

Final Rule.

Whenever the primary and secondary rules fail , the best j , k pair will be selected which

satisfies the conditions k = 4 and the number of lines in the display is maximum but less

than 30.

4.1.2 Statistical Plots.

STATPLOTS

STATPLOTS of column (C)

A STATPLOTS instruction produces four very useful plots for an exploratory analysis of

data. The plots can be very helpful for detecting any anomalies in the data such as nonrandomness, lack of independence, presence of outliers, etc.

72
Plot 1, in the upper left-hand comer, is a simple plot of the measurements X(i) vs. the row number i, or the order the measurements are entered into the worksheet. This plot may

be used to detect many different patterns of non -randomness such as trends, outliers, etc.

Plot 2, in the upper right-hand comer, is an autoregressive plot of X (i) versus X(i-l).

The plot is particularly useful for assessing lack of independence in the data. If the

measurements are well behaved, the plot should not show any recognizable pattern.

Plot 3, in the lower left-hand comer, is a histogram. The histogram, designed by J. J. Filliben, is somewhat different from a conventional histogram. The interval for the

horizontal axis is 0.2 standard deviations so that the horizontal axis goes from -5 to +5

standard deviations. Two scales are shown: one for the values of the measurements and one below it for multiples of standard deviations from the mean. Frequency is shown on the

vertical axis. Information is printed below the histogram which gives the number of

measurements, the value of the 0.2 standard deviation class width, the number of

observations in excess of the mean plus or minus 5 standard deviations, and the number of

measurements represented by a plotted X in the histogram.

Plot 4, in the lower right-hand comer, is a normal probability plot. Section C4.7.1

gives a description of the advantages and uses of a probability plot. An assumption of a normal distribution of measurements is not satisfied, if the points do not lie approximately

on a straight line. Any marked curvature indicated by the points in the plot is a sign of

non-normality. Values of the probability plot correlation coefficient (discussed in Section

C4.7.1 ), the scrawl (discussed in Section C4.1.1), the mean and the standard deviation are printed at the bottom of the plot.

The vertical and horizontal scales for all four plots are determined by the minimum and

maximum values plotted. The plotting symbol for plots 1, 2 and 4 is a period (.). If two or more points coincide, the plotting symbol becomes an asterisk (*)

.

The plotting symbol

for plot 3 (histogram) is an X. Depending upon the frequency indicated on the vertical

axis, any X may represent 1 or more measurements. The maximum number of measurements

represented by an X is given in the last line of information at the bottom of the histogram.

Effect of WIDTH and LENGTH

A LENGTH instruction has no effect on a STATPLOTS instruction. If the width, as determined by a WIDTH, INTERACTIVE or TERMINAL instruction, is less than 120, then plots 1 and 2 are printed on one page and plots 3 and 4 are printed on the next page. The

information printed below the normal probability plot appears below plot 2 on the first

page. The information printed below the histogram is printed below the normal probability

plot on the next page.

Remark

Serious consideration was given to making the results of STATPLOTS part cf the atuomatic

printing of a STATISTICAL analysis instruction. It was finally decided that there should be

a separate instruction. However, the user of a STATISTICAL analysis would be well advised

to use a STATPLOTS instruction in conjunction with a STATISTICAL analysis instruction in

most instances.

Remark

Four plots on one page are far more effective than four plots on separate pages. By having all the plots on one page the eye can survey all plots simultaneously. A plot may

display evidence not obvious in the other plots. Sometimes one plot can be used to support evidence found in another plot. Occasionally, the evidence in two plots may seem to conflict. This frequently suggests that further analysis or experimentation is needed.

73
Restrictions

1. The number of measurements (NRMAX) must be at least 5.

2. The number of measurements (NRMAX) may not exceed 6250.

Example

The set of instructions listed below is used to show the use of STATPLOTS to analyze a

set of 200 steel beam deflection measurements. The results are show on pages 75, 76 and 77.

SET BEAM DEFLECTION DATA IN COLUMN 1 -213 -564 -035 -015 +141 +115 -420 -360 +203 -338

-431 +194 -220 -513 +154 -125 -559 +092 -021 -579

-052 +099 -543 -175 +162 -457 -346 +204 -300 -474

+164 -107 -572 -008 +083 -541 -224 +180 -420 -374

+201 -236 -531 +083 +027 -564 -112 +131 -507 -254

+199 -311 -495 +143 -046 -579 -090 +136 -472 -338

+202 -287 -477 +169 -124 -568 +017 +048 -568 -135

+162 -430 -422 +172 -074 -577 -013 +092 -534 -243

+194 -355 -465 +156 -081 -578 -064 +139 -449 -384

+193 -198 -538 +110 -044 -577 -006 +066 -552 -164

+161 -460 -344 +205 -281 -504 +134 -028 -576 -118

+156 -437 -381 +200 -220 -540 +083 +011 -568 -160

+172 -414 -408 +188 -125 -572 -032 +139 -492 -321

+205 -262 -504 +142 -083 -574 -000 +048 -571 -106

+137 -501 -266 +190 -391 -406 +194 -186 -553 +083

-013 -577 -049 +103 -515 -280 +201 +300 -506 +131

-045 -578 -080 +138 -462 -361 +201 -211 -554 +032

+074 -533 -235 +187 -372 -442 +182 -147 -566 +025

+068 -535 -244 +194 -351 -463 +174 -125 -570 +015

+072 -550 -190 +172 -424 -385 +198 -218 -536 +096

STATPLOTS OF COLUMN 1 WIDTH 80

STATPLOTS OF COLUMN 1

74
OMNITAB

2/19/76

STATPLOTS

OF

STEEL

BEAM

DEFLECTION

DATA

PAGE

£

;

KH

OONCO^O'ONHinOlMNHinO^KlNHinO^ O ID H o N CO n i OlOlIOCOtNNHuDO'tailON

to CM N H H • i HHNNtOtO't'J^tOLO

OvONCO^OONHLOOltONHlOCntONHLnai O m HO N CO to • LOCMOCOtMNHiOOM-OltON

tO CM (M H H i l HHNCMtOtO’J^^lOW

xxxxxxxxx

OONCOHOOP'HlOOHONHLOCntONHmai O U1 H O (M CO tO I LOOWOoOtMNHlOOM-CTltON

tO N N H H I i HHCMCMtOtOM-sf^uTLO

OONCO^OONCO^OONCO^OOMCO^O

N H O

75

NUMBER

OF

MEASUREMENTS

=

200

.

PROBABILITY

PLOT

CORRELATION

COEFFICIENT

=

.9540

CLASS

WIDTH

=

.2

STANDARD

DEVIATIONS

=

55.466433

.

SCRAWL

(N=

200,

*=

-579.00000,

H=

-453.00000,

0

OBSERVATIONS

WERE

IN

EXCESS

OF5

STANDARD

DEVIATIONS

.

M=

-162.00000,

H=

94.000000,

*=

300.00000)

ABOUT

THE

SAMPLE

MEAN

AND

WERE

NOT

PRINTED

IN

HISTOGRAM

.

MEAN

=

-177.43500

EACH

X

REPRESENTS

BETWEEN

1

AND

2

MEASUREMENTS

.

STANDARD

DEVIATION

=

277.33216
OMNITAB 2/19/76 STATPLOTS OF STEEL BEAM DEFLECTION DATA

PLOT OF X(I) VERSUS I + 300.00 + + 256.05 - - 212.10 -

. - 168.15 - * . • . • . 124.20 . * ... • • . - 80.25 + • • * • • • . • , . + 36.30 - * . • • . -7.65 - . • . . • . - -51.60 — . , • . . - -95.55 -

. . • • • • • . - -139.50 + • • • • . + -183.45 -

. . , . . -227.40 . . • , . -271.35 - . . . . . • - -315.30 -

. , , - -359.25 + *

• * . • • • . . + -403.20 -

. • • * ft . -447.15 -

• • • • . • . . . -491.10 -

• • • • • • ... - -535.05 - ... . . , , • . . . • . . - -579.00 + . +

+ + + + + + 1.0 40.8 80.6 120.4 160.2 200.0

PLOT OF X(I) VERSUS X(I-l)

+ + + + - + + 300.00 + . + 256.05 - - 212.10 - *** * - 168.15 - ft * ft - 124.20 ft ftft - 80.25 + ** * ft + 36.30 _ * • • • - -7.65 _* ft - -51.60 .* • • • - -95.55 _ft

.

ft - -139.50 + .* * + -183.45 * ft - -227.40 * ft - -271.35 * * - -315.30 -

. , - -359.25 + * * ft + -403.20 - * ft - -447.15 -

. * ftft - -491.10 - *

.

ft

. - -535.05 - ft*ft ftftft - -579.00 + . ft ft ft ft ft ft +

+ + + + + + -579. -403.2 -227.4 -51.6 124.2 300.

PROBABILITY PLOT CORRELATION COEFFICIENT = .9540

SCRAWL (N= 200, *= -579.00000, 11= -453.00000,

M= -162.00000, H= 94.000000, *= 300.00000)

MEAN = -177.43500

STANDARD DEVIATION = 277.33216

76
OMNITAB 2/19/76 STATPLOTS OF STEEL BEAM DEFLECTION DATA 3

HISTOGRAM

+ + + o + + + 28.0 + X + 26.6 - X

- 25.2 - X X

- 23.8 - X X

- 22.4 - X X

- 21.0 + X X + 19.6 - X XX - 18.2 - X XX - 16.8 - X XX - 15.4 - X XX - 14.0 + XX XX + 12.6 - xxxx X XXX - 11.2 - xxxx X XX XXX - 9.8 - xxxx X XXX XXX - 8.4 - xxxxx X xxxxxxx - 7.0 + xxxxxxx xxxxxxx + 5.6 - xxxxxxxxxxxxxxx - 4.2 - xxxxxxxxxxxxxxx - 2.8 - xxxxxxxxxxxxxxx - 1.4 - XXXXXXXXXXXXXXX X - 0.0 + XXXXXXXXXXXXXXX X +

+ - - +- - + —--0 + +-- + -1564

,

,096 -1009.431 -454.7672 99.89716 654.5615 1209.226

-5 -3 -1 1 3 5 NUMBER OF MEASUREMENTS = 200

CLASS WIDTH = .2 STANDARD DEVIATIONS = 55.466433

0 OBSERVATIONS WERE IN EXCESS OF 5 STANDARD DEVIATIONS

ABOUT THE SAMPLE MEAN AND WERE NOT PRINTED IN HISTOGRAM

EACH X REPRESENTS BETWEEN 1 AND 2 MEASUREMENTS

+ 300.00 + 256.05 - 212.10 - 168.15 - 124.20 - 80.25 + 36.30 - -7.65 - -51.60 - -95.55 - -139.50 + -183.45 - -227.40 - -271.35 - -315.30 - -359.25 + -403.20 - -447.15 - -491.10 - -535.05 - -579.00 + .+ - -2.,701

NORMAL PROBABILITY PLOT

+ + o + +

.+ *****

  ** *

** + **

 

** 

** +

* **

* * ** + **

** ***

***

* ***** +

+ + o + + —

+

-1.621 -.5402 .5402 1.621 2.701

77
4.1.3 Table Making .

NTABLE AVERAGE, NTABLE FREQUENCY,

NTABLE MINIMUM, NTABLE PERCENTAGE,

NTABLE STDDEV, NTABLE SUM,

TABLE MAXIMUM, TABLE MEDIAN,

TABLJE PROPORTION, TABLE RANGE,

NTABLE MEDIAN, NTABLE MAXIMUM,

NTABLE PROPORTION, NTABLE RANGE,

TABLE AVERAGE, TABLE FREQUENCY,

TABLE MINIMUM, TABLE PERCENTAGE

TABLE STDDEV, TABLE SUM

A collection of data is often classified according to several variables, each of which

can assume discrete values. For example, people can be classified by sex, marital status,

education, etc. Sex, marital status, education, etc. will be called variables. The values

that a variable may have will be called levels. The levels must have numerical values. The

numbers 0 and 1 may be used to denote the two levels of sex. The interpretation of the data

is aided considerably by cross tabulation or the construction of a table. The most common

table is one which clearly shows the frequency (or count) for every possible combination in

the classification. The table will show, for example, how many of the persons in a study

are male, divorced and have a college education. Less frequently, but still important,

there is a response variable, i.e., salary, and a table of a statistic of the response

variable is needed. For example, a table showing the average salary for divorced males with

a college education, single females with an advanced degree, etc., may be needed.

A large and powerful set of instructions for constructing and printing tables (cross

tabulation) has been added to OMNITAB, which should find wide use in many different areas. Each command consists of two words. The first word is either TABLE or NTABLE. The N before

TABLE in NTABLE has the usual significance; i.e., the printing does not start on a new page

and titles are not printed. The second word in the command may be any of the ten words

AVERAGE, FREQUENCY, MEDIAN, MAXIMUM, MINIMUM, PERCENTAGE , PROPORTION, RANGE, STDDEV, or SUM.

There is an optional form of each instruction which permits automatic storage.

Data for examples

Examples are given below which use the following instructions and data:

READ DATA INTO COLUMNS 7 5 27 50

$ 6 25 40 101

7 23 48 107

7 25 40 104

6 25 49 109

6 26 40 106

7 24 48 102

6 26 40 103

6 25 49 108

6 23 49 105

7 24 48 108

7 24 48 102

6 25 49 101

7 26 48 108

7 25 48 103

7 26 49 107

WIDTH 80

HEAD COL 5/ AGE

TABLE FREQUENCY 3 WAY FOR DATA IN COLUMNS 7 5 AND 27

ADD 0.98765 TO COLUMN 27 AND PUT IN COLUMN 28

78
Instructions

NTABLE AVERAGE (n) way with levels in cols (C) , (C) , . .

. , (C) for data in (C)

NTABLE FREQUENCY (n) way with levels in cols (C) , (C) , . .

. , (C)

NTABLE MAXIMUM (n) way, levels in cols (C) , (C) , . .

. , (C) for data in col (C)

The result of using the instructions

SPACE 5 NTABLE MAXIMUM 3 WAY FOR DATA IN COLUMNS 7, 5, 28 and 50

is shown at the bottom of page 89.

NTABLE MEDIAN (n) way with levels in cols (C) , (C) , . .

.

,

(C) for data in col (C)

The result of using the instructions

SPACE 5 NTABLE MEDIAN 3 WAY FOR DATA IN COLUMNS 7, 5, 28 and 50

is shown at the bottom of page 90.

NTABLE MINIMUM (n) way with levels in cols (C) , (C) , . .

.

, (C) for data in col (C)

NTABLE PERCENTAGE (n) way with levels in cols (C) , (C)

,

NTABLE PROPORTION (n) way with levels in cols (C) , (C)

,

The result of using the instructions

SPACE 5 NTABLE PROPORTION 3 WAY FOR DATA IN COLUMNS 7, 5, 28

is shown at the bottom of page 91.

NTABLE RANGE (n) way with levels in cols (C) , (C) (C) for data in col (C)

79
NTABLE STDDEV (n) way with levels in cols (C) , (C) , . .

. , (C) for data in col (C)

The result of using the instructions

SPACE 5 NTABLE STDDEV 3 WAY FOR DATA IN COLUMNS 7, 5, 28 and 50

is shown at the bottom of page 88

.

NTABLE SUM (n) way with levels in cols (C) , (C) , . .

.

,

(C) for data in col (C)

The result of using the instructions

SPACE 5 NTABLE SUM 3 WAY FOR DATA IN COLUMNS 7, 5, 27 and 50

is shown at the bottom of page 87.

: TABLE AVERAGE (n) way with levels in cols (C) , (C) , . . . , (C) for data in col (C)

The result of using the instruction

TABLE AVERAGE 3 WAY FOR DATA IN COLUMNS 7, 5, 27 and 50

is shown at the top of page 88

.

: TABLE FREQUENCY (n) way with levels in cols (C) , (C) , . .

. , (C)

The result of using the instruction

TABLE FREQUENCY 3 WAY FOR DATA IN COLUMNS 7, 5, and 27

is shown on page 82. To show the effect of varying the number of variables, the result of

lasing the instruction

TABLE FREQUENCY 4 WAY FOR DATA IN COLUMNS 7, 5, 27 and 50

is shown on pages 83 and 84 and the results of using the instructions

TABLE FREQUENCY 2 WAY FOR DATA IN COLUMNS 5 and 27

SPACE 3 NTABLE FREQUENCY 1 WAY FOR DATA IN COLUMN 5

are shown on page 87. The result of using the instructions

WIDTH 50

TABLE FREQUENCY 3 WAY FOR DATA IN COLUMN 7, 50 and 27

is shown on pages 85 and 86. In this case, the number of levels of the second variable, 9,

is too large to print the table on a page with 50 characters per line. The first five

80
columns are printed on page 85 and the remaining four columns and the totals are printed on page 86.

TABLE MAXIMUM (n) way with levels in cols (C) , (C) ,

.

•

. , (C) for data in col (C)

TABLE MEDIAN (n) way with levels in cols (C) , (C) , .

.

-,(C) for data in col (C)

TABLE MINIMUM (n) way with levels in cols (C) , (C) ,

.

..,(C) for data in col (C)

The result of using the instruction

TABLE MINIMUM 3 WAY FOR DATA IN COLUMNS 7, 5, 27 and 50

is shown at the top of page 89.

: TABLE PERCENTAGE (n) way with levels in cols (C) , (C) , . . . , (C)

The result of using the instruction

TABLE PERCENTAGE 3 WAY FOR DATA IN COLUMNS 7, 5, and 27

is shown at the top of page 91.

: TABLE PROPORTION (n) way with levels in cols (C) , (C) , . . . , (C)

: TABLE RANGE (n) way with levels in cols (C) , (C) , . .

. , (C) for data in col (C)

The result of using the instruction

TABLE RANGE 3 WAY FOR DATA IN COLUMNS 7, 5, 27 and 50

is shown at the top of page 90.

: TABLE STDDEV (n) way with levels in cols (C) , (C) , . .

. , (C) for data in col (C)

TABLE SUM (n) way with levels in cols (C) , (C) , . .

. , (C) for data in col (C)

81
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. PG 1

3-WAY TABLE OF FREQUENCIES OF 15 MEASUREMENTS

(1) = COLUMN 7 , 2 LEVELS

(2) = AGE , 4 LEVELS

(3) = COLUMN 27 , 3 LEVELS

(2) 23 24 25 26 TOTALS

(3) (1)

40 6 0 0 1 2 3

7 0 0 1 0 1

48 6 0 0 0 0 0

7 1 3 1 1 6

49 6 1 0 3 0 4

7 0 0 0 1 1

TOTALS 2 3 6 4 15

82
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. PG 3

4 -WAY TABLE OF FREQUENCIES OF 15 MEASUREMENTS

(1) = COLUMN

(2) = AGE

(3) = COLUMN

(4) = COLUMN

7 , 2 LEVELS

, 4 LEVELS

27 , 3 LEVELS

50 , 9 LEVELS

(2) 23 24 25 26 TOTALS

(4) (3) CD

101 40 6 0 0 1 0 1

7 0 0 0 0 0

48 6 0 0 0 0 0 7 0 0 0 0 0

49 6 0 0 1 0 1

7 0 0 0 0 0

102 40 6 0 0 0 0 0

7 0 0 0 0 0

48 6 0 0 0 0 0

7 0 2 0 0 2

49 6 0 0 0 0 0

7 0 0 0 0 0

103 40 6 0 0 0 1 1

7 0 0 0 0 0

48 6 0 0 0 0 0

7 0 0 1 0 1

49 6 0 0 0 0 0

7 0 0 0 0 0

104 40 6 0 0 0 0 0

7 0 0 1 0 1

48 6 0 0 0 0 0 7 0 0 0 0 0

49 6 0 0 0 0 0

7 0 0 0 0 0

105 40 6 0 0 0 0 0

7 0 0 0 0 0

48 6 0 0 0 0 0

7 0 0 0 0 0

49 6 1 0 0 0 1 7 0 0 0 0 0

83
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. PG 4

CONTINUATION

(2) 23 24 25 26 TOTALS

(4) (3) (1)

106 40 6 0 0 0 1 1 7 0 0 0 0 0

48 6 0 0 0 0 0

7 0 0 0 0 0

49 6 0 0 0 0 0

7 0 0 0 0 0

107 40 6 0 0 0 0 0

7 0 0 0 0 0

48 6 0 0 0 0 0

7 1 0 0 0 1

49 6 0 0 0 0 0

7 0 0 0 1 1

108 40 6 0 0 0 0 0

7 0 0 0 0 0

48 6 0 0 0 0 0

7 0 1 0 1 2

49 6 0 0 1 0 1

7 0 0 0 0 0

109 40 6 0 0 0 0 0

7 0 0 0 0 0

48 6 0 0 0 0 0

7 0 0 0 0 0

49 6 0 0 1 0 1 7 0 0 0 0 0

TOTALS 2 3 6 4 15

84
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRU5

3-WAY TABLE OF FREQUENCIES OF 15 MEASUREMENTS

(1) = COLUMN 7 , 2 LEVELS

(2) = COLUMN 50 , 9 LEVELS

(3) = COLUMN 27 , 3 LEVELS

(2) 101 102 103 104 105

(3) (1)

40 6 1 0 1 0 0

7 0 0 0 1 0

48 6 0 0 0 0 0

7 0 2 1 0 0

49 6 1 0 0 0 1

7 0 0 0 0 0

TOTALS 2 2 2 1 1

85
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRU6

3 -WAY TABLE OF FREQUENCIES OF 15 MEASUREMENTS

CONTINUATION

(1) = COLUMN 7 , 2 LEVELS

(2) = COLUMN 50 , 9 LEVELS

(3) = COLUMN 27 , 3 LEVELS

(2) 106 107 108 109 TOTALS

(3) (1)

40 6 1 0 0 0 3

7 0 0 0 0 1

48 6 0 0 0 0 0

7 0 1 2 0 6

49 6 0 0 1 1 4

7 0 1 0 0 1

TOTALS 1 2 3 1 15

86
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. 7

2 -WAY TABLE OF FREQUENCIES OF 15 MEASUREMENTS

(1) = AGE 9 4 LEVELS

(2) = COLUMN 27 , 3 LEVELS

(2) 40 48 49 TOTALS

CD

23 0 1 1 2 24 0 3 0 3 25 2 1 3 6 26 2 1 1 4

TOTALS 4 6 5 15

1-WAY TABLE OF FREQUENCIES OF 15 MEASUREMENTS

CD

(D

= AGE , 4 LEVELS

23 2 24 3

25 6 26 4

TOTAL 15

3-WAY TABLE OF SUMS OF 15 MEASUREMENTS IN COLUMN 50

(D = COLUMN 7 , 2 LEVELS

(2) = AGE 9 4 LEVELS

(3) = COLUMN 27 , 3 LEVELS

(2) 23 24 25 26 TOTALS

(3) (D

40 6

* * 101 209 310

7 * * 104 * 104

48 6

* * * * *

7 107 312 103 108 630

49 6 105 * 318 * 423

7 * * * 107 107

TOTALS 212 312 626 424 1574

* 14 OF THE 24 CELLS IN THE TABLE ARE EMPTY.

87
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. 8

3 -WAY TABLE OF AVERAGES OF 15 MEASUREMENTS IN COLUMN 50

(1) = COLUMN 7 y 2 LEVELS

(2) = AGE y 4 LEVELS

(3) = COLUMN 27 y 3 LEVELS

(2) 23 24 25 26

(3) (1)

40 6

* * 101.0 104.5

7 * * 104.0 a

48 6

* * A A

7 107.0 104.0 103.0 108.0

49 6 105.0 a 106.0 A

7

a * a 107.0

14 OF THE 24 CELLS IN THE TABLE ARE EMPTY.

3-WAY TABLE OF STANDARD DEVIATIONS OF 15 MEASUREMENTS IN COLUMN 50

(1) = COLUMN 7 , 2 LEVELS

(2) = AGE , 4 LEVELS

(3) = COLUMN 28 , 3 LEVELS

(2) 23 24 25 26

(3) (1)

40.988 6

A * .0000 2.1213

7 A * .0000 A

48.988 6

A A A A

7 .i0000 3.4641 .0000 .0000

49.988 6 .i0000 * 4.3589 A

7

A A A .0000

* 14 OF THE 24 CELLS IN THE TABLE ARE EMPTY.

88
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. 9

3 -WAY TABLE OF MINIMUMS OF 15 MEASUREMENTS IN COLUMN 50

CD = COLUMN 7 , 2 LEVELS

(2) = AGE 4 LEVELS

(3) = COLUMN 27 , 3 LEVELS

(2) 23 24 25 26 TOTALS

(3) (1)

40 6

A * 101 103 101

7

A * 104 * 104

48 6

A * * A A 7 107 102 103 108 102

49 6 105 A 101 * 101

7

A A A 107 107

TOTALS 105 102 101 103 101

* 14 OF THE 24 CELLS IN THE TABLE ARE EMPTY.

3-WAY TABLE OF MAXIMUMS OF 15 MEASUREMENTS IN COLUMN 50

(1) = COLUMN 7 , 2 LEVELS

(2) = AGE. , 4 LEVELS

(3) = COLUMN 28 , 3 LEVELS

(2) 23 24 25 26 TOTALS

(3) (1)

40.988 6

* A 101 106 106

7

* A 104 A 104

48.988 6

it A A A A

7 107 108 103 108 108

49.988 6 105 A 109 A 109

7

A A A 107 107

TOTALS 107 108 109 108 109

* 14 OF THE 24 CELLS IN THE TABLE ARE EMPTY.

89
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. 11

3-WAY TABLE OF RANGES OF 15 MEASUREMENTS IN COLUMN 50

(1) = COLUMN 7 , 2 LEVELS

(2) = AGE , 4 LEVELS

(3) = COLUMN 27 , 3 LEVELS

(2) 23 24 25 26 TOTALS

(3) U)

40 6

* * 0 3 5

7

* * 0

A 0

48 6

* A A A A

7 0 6 0 0 6

49 6 0

* 8 A

8

7 A * A 0 0

TOTALS 2 6 8 5 8

* 14 OF TIE 24 CELLS IN THE TABLE ARE EMPTY.

3 -WAY TABLE OF MEDIANS OF 15 MEASUREMENTS IN COLUMN 50

(1) = COLUMN 7 , 2 LEVELS

(2) = AGE ,4 LEVELS

(3) = COLUMN 28 , 3 LEVELS

(3)

(2)

(1)

23 24 25 26

40.988 6

A A 101.0 104.5

7

A A 104.0 A

48.988 6

A A A A

7 107.0 102.0 103.0 108.0

49.988 6 105.0 A 108.0 A

7

A A A 107.0

* 14 OF THE 24 CELLS IN TIE TABLE ARE EMPTY.

90
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. 12

3 -WAY TABLE OF PERCENTAGES OF 15 MEASUREMENTS

(1) = COLUMN 7 , 2 LEVELS

(2) = AGE y 4 LEVELS

(3) = COLUMN 27 , 3 LEVELS

(2) 23 24 25 26 TOTALS

(3) (1)

40 6 .00 .00 6.67 13.33 20.00

7 .00 .00 6.67 .00 6.67

48 6 .00 .00 .00 .00 .00

7 6.67 20.00 6.67 6.67 40.00

49 6 6.67 .00 20.00 .00 26.67

7 .00 .00 .00 6.67 6.67

TOTALS 13.33 20.00 40.00 26.67 100.00

3 -WAY TABLE OF PROPORTIONS OF 15 MEASUREMENTS

(1) = COLUMN 7 , 2 LEVELS

(2) = AGE , 4 LEVELS

(3) = COLUMN 28 , 3 LEVELS

(3)

(2)

(1)

23 24 25 26 TOTALS

40.988 6 .0000 .0000 .0667 .1333 .2000

7 .0000 .0000 .0667 .0000 .0667

48.988 6 .0000 .0000 .0000 .0000 .0000

7 .0667 .2000 .0667 .0667 .4000

49.988 6 .0667 .0000 .2000 .0000 .2667

7 .0000 .0000 .0000 .0667 .0667

TOTALS .1333 .2000 .4000 .2667 1.0000

91
Title

Each table has a one line title at the top which gives (i) the number of variables in

the classification, (ii) the statistic tabulated, and (iii) the number of measurements

(NRMAX) . The coding scheme for the variables is shown below the title and includes the

column number where the levels of the variable are stored and the number of levels for each

of the variables. If a LABEL or HEAD instruction has been used, the heading is used instead

of the column number.

Use of asterisks

A distinction is made between zero and nothing for all the tables; except those for

frequencies, percentages and proportions. When no data exists for a specific combination of

levels of variables, an asterisk is printed in the table. In the table of standard

deviations on page 88 a zero is printed when there is only one measurement (or all the

measurements are the same), but an asterisk is printed when the frequency is zero. A note

is printed at the bottom of the table showing the number of cells in the table which are

empty.

Row and column totals printed

Row and column totals are printed for the TABLE (or NTABLE) FREQUENCY, MINIMUM, MAXIMUM,

PERCENTAGES, PROPORTIONS, RANGE, and SUM instructions. The totals are not printed for the

AVERAGE, MEDIAN or STDDEV instructions. The row totals are not printed for 1-way tables and

no totals are printed for 1-way tables when the variable has a single level. For 2-way

tables the row totals can be used directly. For 3 -way and higher tables, the row totals can

be summed by hand to obtain the different marginal totals.

The meaning of a frequency total is obvious. The meaning of any of the totals in a table of ranges may not be quite so obvious. In the table of ranges on page 90, the number

5 in the first row of the row totals is the difference between the largest number in column

50 corresponding to level 40 in column 27, 106, and the smallest, 101.

Number of digits printed

Two decimal places are used to print the entries in the table produced by a TABLE (or

NTABLE) PERCENTAGE instruction and four decimal places are used for a TABLE (or NTABLE)

PROPORTIONS instruction. For all the other instructions and the levels of the variables,

the maximum number of digits printed is five. The number of digits needed to print a number

is computed. If a number can be printed with less than five digits, it is printed with the

minimum number of digits required. Note, the difference in the printing of the levels of

column 27 (40, 48 and 49) and the levels of column 28 (40.988, 48.988 and 49.988).

Space for table

The tables are printed compactly without any waste. The actual size will vary depending

upon the number of digits needed to print the numbers. Since three decimal places are needed to print the levels in column 28 and none are needed to print the levels in column

27, more space is used to print the table on page 94 than the table on page 82.

Number of arguments

The first argument in any of the instructions is always, (n) , the number of variables in

the classification. The remaining arguments are always column numbers. The instructions

for tabulating frequencies, percentages and proportions have (n+1) arguments, or (n+2)

arguments if storage is requested. The instructions for tabulating averages, maximums,

medians, minimums, ranges, standard deviations and sums always have (n+2) arguments, or

(n+3) arguments if storage is requested.

92
Restriction

The value of the first argument, (n) , must be greater than zero. It must be less than

or equal to eight in the instructions which table frequencies, percentages and proportions.

It must be less than or equal to seven in the instructions which table averages, maximums,

medians, minimums, ranges, standard deviations and sums.

Remarks

Often data has to be preprocessed before a table is constructed. There may be missing

observations. A variable may need to be coded. OMNITAB has an extremely powerful set of

instructions for manipulating data. The editing instructions described in section C3.5.1

can be particularly useful for preprocessing data for cross tabulation.

If the second variable has a large number of levels, the page may not be wide enough to

print all the columns on one page as can be seen on pages 85 and 86. It may be desirable to

make the second variable the one with the fewest (or a small) number of levels.

Storage

Each of the ten TABLE and the ten NTABLE instructions has an optional form with one

additional argument to specify automatic storage. Data is stored in (n+l+r) columns,

starting with the last column number (last argument) , where r is the number of levels of the

second variable.

Page 94 shows the results of using the instructions:

ADD 0.98765 TO COLUMN 27 AM) PUT IN COLUMN 28

TABLE FREQUENCY 3 WAY FOR DATA IN COLUMNS 7, 5 and 28 STORE 31

SPACE 3 NPRINT COLUMNS 31 *** 35

NPRINT COLUMNS 36 *** 38

Items stored in the first column are:

Row Description

1 length of table

2 number of columns in table

3 total size of table

4 number of columns of stored data

5 starting column for storing levels

6 last column of stored data

The table is stored in the next (r) columns. The last (n) columns contain the number of

levels in the first row and the values of the levels for each of the (n) variables in rows below the first row.

The first row of columns 36 to 38 contain the numbers 2 , 4 and 3 which are the numbers

of levels of the variables stored in columns 7, 5 and 28. The number 6 in row 1 of column

31 equals 2x3 equals the number of rows in the table. The number 24 in row 3 of column

equals the product of the numbers of levels (2x4x3), which is the size of the table. The

number of columns of storage in row 4 of column 31 is 8 = 1+4+3. Rows 5 and 6 of column 31

contain the starting column, 36, and the last column, 38, for storing the values of the

levels of each of the variables.

93
OMNITAB 3/12/77 EXAMPLES OF TABLE, NTABLE INSTRUCTIONS. PG 2

3-WAY TABLE OF FREQUENCIES OF 15 MEASUREMENTS

(1) = COLUMN 7 , 2 LEVELS

(2) = AGE > 4 LEVELS

(3) = COLUMN 28 , 3 LEVELS

(2) 23 24 25 ;26 TOTALS

(3) CD

40.988 6 0 0 1 2 3

7 0 0 1 0 1

48.988 6 0 0 0 0 0

7 1 3 1 1 6

49.988 6 1 0 3 0 4

7 0 0 0 1 1

TOTALS 2 3 6 4 15

6.0000000 0. 0. 1.0000000

4.0000000 0. 0. 1.0000000

24.000000 0. 0. 0. 8.0000000 1.0000000 3.0000000 1.0000000

36.000000 1.0000000 3.0000000

38.000000

2.0000000 4.0000000 3.0000000

6.0000000 23.000000 40.987650

7.0000000 24.000000 48.987650

25.000000 49.987650

26.000000

2.0000000

0 . 0 . 1.0000000

0 .

1.0000000

94
The structure of the optional forms which specify automatic storage is shown below.

/ 7

/ NTABLE AVERAGE (n) way, levels in (C) . . . (C)

,

data in (C) start storing in (C) / 7 /

/ /

/ NTABLE FREQUENCY (n) way, levels in cols (C) , (C) , . .. , (C) start storing in (C) /

7 7

/ 7

/ NTABLE MAXIMUM (n) way, levels in (C) . .. (C) data in (C) start storing in (C) /

7 /

/ /

/ NTABLE MEDIAN (n) way, levels in (C) , . . . , (C) data in (C) start storing in (C) /

7 7

/ /

/ NTABLE MINIMUM (n) way, levels in (C) . . . (C) data in (C) start storing in (C) /

7 7

/ /

/ NTABLE PERCENTAGE (n) way, levels in cols (C) , (C) , . .

. , (C) start storing in (C) /

7 7

/ /

/ NTABLE PROPORTION (n) way, levels in cols (C) , (C) , . .

. , (C) start storing in (C) /

/ /

t I / NTABLE RANGE (n) way, levels in (C) , . .

. , (C) data in (C) start storing in (C) /

7 /

t I / NTABLE STDDEV (n) way, levels in cols (C) . .

. (C) data in (C) start storing in (C) /

7 /

I I / NTABLE SUM (n) way, levels in cols (C) , . .

. , (C) data in (C) start storing in (C) /

7 /

I I / TABLE AVERAGE (n) way, levels in (C) , . .

. , (C) data in (C) start storing in (C) / 7 7

/ I

/ TABLE FREQUENCY (n) way, levels in cols (C) , (C) , . . . , (C) start storing in (C) /

/ /

95
/ 7

/ TABLE MAXIMUM (n) way, levels in (C) , . . . , (C) data in (C) start storing in (C) /

/ /

/ /

/ TABLE MEDIAN (n) way , levels in (C) , . .

. , (C) data in (C) start storing in (C) /

/ /

/ 7

/ TABLE MINIMUM (n) way, levels in (C) , . .. , (C) data in (C) start storing in (C) /

/ 7

/ /

/ TABLE PERCENTAGE (n) way, levels in cols (C) , (C) , . .. , (C) start storing in (C) /

/ 7

/ /

/ TABLE PROPORTION (n) way, levels in cols (C) , (C) , . .

. , (C) start storing in (C) /

/ /

/

' /

/ TABLE RANGE (n) way, levels in cols (C) , . .. , (C) data in (C) start storing in (C) /

/ /

/ /

/ TABLE STDDEV (n) way, levels in (C) , . .

. , (C) data in (C) start storing in (C) /

/ /

/ /

/ TABLE SUM (n) way, levels in cols (C) , . .

. , (C) data in (C) start storing in (C) /

/ 7

4.2* Analysis Of One Column Of Data .

The automatic printing and storage of a STATISTICAL analysis instruction has been

greatly improved. However, none of the nine forms of the instruction has been changed.

Printing .

The automatic printing has been greatly improved by (1) using readable printing rather

than scientific notation, (2) limiting the printing to 72 characters per line so that the

instruction can be used with a narrow carriage terminal , (3) deleting statistics of little

or no value, (4) adding statistics of substantial value, and (5) changing the style.

Without the "QMNITAB" title line at the top of the page, pages 99 and 100 show the

automatic printing for the example on page 115 of TN 552 ("OMNITAB II User's Reference

Manual," National Bureau of Standards Technical Note 552). In effect, they replace pages

117 and 118 of TN 552. Below are the two lines which would replace the first line at the

top after STATISTICAL ANALYSIS OF ..., if weights of 1.0 in column 7 were specified.

96
WEIGHTS IN COLUMN 7 OF LENGTH 39 WITH 39 NON- ZERO WEIGHTS

ALL COMPUTATIONS ARE BASED ON OBSERVATIONS WITH NON-ZERO WEIGHTS

Page 101 shows the second page of printing for the same data when weights of 1.0 are specified.

Page 102 shows the first page of the printing for the example discussed in "A User's

I Guide to the OMNITAB Command 'STATISTICAL ANALYSIS'," NBS TO 756.

Note that HEAD COL 2/ PIKES PEAK was used and PIKES PEAK is used after "STATISTICAL ANALYSIS

OF" instead of COLUMN 2.

Changes in Automatic Printing

(1) Statistics deleted (all from OTHER STATISTICS) are:

WTD SUM OF VALUES

WTD SUM OF SQUARES

WTD SUM OF DEVS SQUARED

STUDENT’S T

WTD SUM ABSOLUTE VALUES

WTD AVE ABSOLUTE VALUES

(2) Statistics added are:

FREQUENCY DISTRIBUTION OF LEAST SIGNIFICANT DIGIT (0,1,..., 9)

INTER-QUARTILE RANGE

QUADRATIC COEFF., SIGNIFICANCE LEVEL

AUTOCORRELATION COEFFICIENT

951 DISTRIBUTION- FREE CONFIDENCE INTERVAL FOR THE MEDIAN

TOLERANCE INTERVALS

SECOND MINIMUM

SECOND MAXIMUM

(MEAN -MINIMUM) /STANDARD DEVIATION

(MAXIMUM-MEAN) /STANDARD DEV IAT ION

LOWER QUARTILE

UPPER QUARTILE

(3) Modifications include:

(i) Column heading determined by HEAD COL is used in the first line.

(ii) The number of distinct measurements is printed, if weights are not

specified.

(iii) The class interval for the frequency distribution is printed.

(iv) Either the weighted mean or the mean is printed, depending upon whether

weights are specified or not, but not both. Similarly, only one midmean, or trimmed mean, is printed.

(v) The absolute value of the coefficient of variation is printed.

(vi) For trend statistics and other tests for non -randomness , a statistic, T,

expected value of the statistic, p(T), and the standard deviation of

the statistic, a(T)

,

have been replaced by the statistic, T, and its

z value, where

97
(vii) The square root of bl, the skewness coefficient, is printed instead of bl.

The sign indicates whether the skewness is positive, i.e., long tail on the right, or negative, i.e., long tail on the left.

Storage .

To avoid any inconvenience to users, the storage in rows 1 to 43 and 51 to 60 has not

been changed; even though some of the statistics have been eliminated from the automatic

printing. The following statistics have been added and may be considered an addendum to page 119 of NBS Technical Note 552.

Row Name Of Statistic Reference

OTHER STATISTICS

44 SECOND MINIMUM Natrella (1963), 17-4

45 SECOND MAXIMUM Natrella (1963), 17-4

46 (MEAN -MINIMUM) /S Natrella (1963), 17-5

47 (MEAN -MAXIMUM)/S Natrella (1963), 17-5

48 SQRT (Bl) Snedecor and Cochran (1967) , 86

49 LOWER QUARTILE Snedecor (1956), 111

50 UPPER QUARTILE Snedecor (1956), 111

MEASURES OF DISPERSION

61 INTER-QUARTILE RANGE Snedecor (1956), 111

TREND STATISTICS

62 QUADRATIC COEFFICIENT Snedecor and Cochran (1967) , 453

63 QUADRATIC SIGNIFICANCE LEVEL Snedecor and Cochran (1967) , 453

OTHER TESTS FOR NON-RANDOMNESS

64 AUTOCORRELATION COEFFICIENT Jenkins and Watts (1968)

,

182

CONFIDENCE INTERVALS

65 LOWER CONFIDENCE LIMIT, MEDIAN Brownlee (1965), 183

66 UPPER CONFIDENCE LIMIT, MEDIAN Brownlee (1965), 183

TOLERANCE INTERVALS

67 LOWER LIMIT, 501 COVERAGE Natrella (1963) ^ Gardner 5 Hull (1966)

68 UPPER LIMIT, 501 COVERAGE Natrella (1963) § Gardner a Hull (1966)

69 LOWER LIMIT, 951 COVERAGE Natrella (1963) § Gardner a Hull (1966)

70 UPPER LIMIT, 951 COVERAGE Natrella (1963) § Gardner a Huii (1966)

71 LOWER LIMIT, 991 COVERAGE Natrella (1963) 8 Gardner a Huii (1966)

72 UPPER LIMIT, 991 COVERAGE Natrella (1963) a Gardner a huh (1966)

73 COVERAGE, 951 DISTRIBUTION FREE Natrella (1963), 2-15

98
STATISTICAL ANALYSIS OF COLUMN 1

NUMBER OF MEASUREMENTS = 39. NO. OF DISTINCT MEASUREMENTS = 17

FREQUENCY 5383775001 DISTRIBUTION WITH 10 CLASSES OF LENGTH .24000000

FREQUENCY 6463444431 DISTRIBUTION OF LEAST SIGNIFICANT DIGIT (0,1,..., 9)

MEASURES OF LOCATION

ARITHMETIC MEAN .41025640

MEDIAN .50000000

MID-RANGE .70000000

MID-MEAN (25 PERCENT TRIMMED MEAN) . . .42380951

MEASURES OF DISPERSION

STANDARD DEVIATION .50668939

AS PERCENT OF MEAN (COEF. OF VAR.) . 123.50554

RANGE 2.4000000

MEAN DEVIATION .40486522

INTER -QUARTI LE RANGE .70000000

VARIANCE .25673414

STANDARD DEVIATION OF MEAN .081135237

TREND STATISTICS

SLOPE, SIGNIFICANCE LEVEL -.0040080961 .585

QUADRATIC COEFF., SIGNIFICANCE LEVEL . .0026019551 .000

OTHER TESTS FOR NON -RANDOMNESS

NUMBER OF RUNS UP AND DOWN, Z VALUE . 23 -1.037

MEAN SQUARE SUCCESSIVE DIFFERENCE . . .28289473

MS SUCC DIFF/2 (VARIANCE) , Z VALUE . .55094880 -2.888

DEVIATIONS FROM ARITHMETIC MEAN

NUMBER OF - SIGNS, + SIGNS .... 19 20

NUMBER OF RUNS, Z VALUE 8 -4.056

AUTOCORRELATION COEFFICIENT .43572843

A TWO-SIDED 95 PERCENT CONFIDENCE INTERVAL FOR

MEAN IS .24600670 TO .57450610

MEDIAN IS .20000000 TO .59999999

S.D. IS .41070743 TO .64762166

THE

(DISTRIBUTION-FREE)

STATISTICAL TOLERANCE INTERVAL WITH 95 PERCENT CONFIDENCE FOR

50 PCT NORMAL COVERAGE IS -.017515361 TO .83802813

95 PCT NORMAL COVERAGE IS -.83254325 TO 1.6530560

99 PCT NORMAL COVERAGE IS -1.2228149 TO 2.0433277

INTERVAL FROM MIN TO MAX HAS DIST.-FREE COVERAGE 88.40

OTHER STATISTICS

MINIMUM -.50000000

SECOND MINIMUM -.40000000

MAXIMUM 1,9000000

SECOND MAXIMUM 1.1000000

(MEAN -MINIMUM) /STANDARD DEVIATION . . 1.7964781

(MAXIMUM-MEAN) /STANDARD DEVIATION . . 2.9401515

SQRT(Bl) , SKEWNESS COEFFICIENT .... .31064667

B2 , KURTOSIS COEFFICIENT 3.3379328

LOWER QUARTILE 0. UPPER QUARTILE .70000000

99
I1

23

4

56

7

8

9 10

111213

1415

16

17

1819

20

21

22

23

2425

26

27

28

29

30

31

32

33

34

35

36

37

38

59

OBSERVATIONS ORDERED OBSERVATIONS

X(I)

.40000000

.59999999

1.0000000

1.0000000

1.0000000

.50000000

.59999999

.70000000

1.0000000

.59999999

.20000000

1.9000000

.20000000

.40000000

0.-.40000000

-.30000000

0 . -.40000000

-.30000000

.10000000

-.10000000

.20000000

-.50000000

.30000000

-.10000000

.20000000

-.20000000

.80000000

.50000000

.59999999

.80000000

.70000000

.70000000

.20000000

.50000000

.70000000

.80000000

1.1000000

RANK X(I) -MEAN NO. X(J) X(J+1)-X(J)

18.5 -.01025641 24 -.50000000 .10000000

24.5 .18974359 16 -.40000000 0.0000000

35.5 .58974360 19 -.40000000 .10000000

35.5 .58974360 17 -.30000000 0.0000000

35.5 .58974360 20 -.30000000 .09999999

21.0 .08974360 28 -.20000000 .10000000

24.5 .18974359 22 -.10000000 0.0000000

28.5 .28974359 26 -.10000000 .10000000

35.5 .58974360 15 0. 0.0000000

24.5 .18974359 18 0. .10000000

14.0 -.21025641 21 .10000000 .10000000

39.0 1.4897436 11 .20000000 0.0000000

14.0 -.21025641 13 .20000000 0.0000000

18.5 -.01025641 23 .20000000 0.0000000

9.5 -.41025640 27 .20000000 0.0000000

2.5 -.81025640 35 .20000000 .09999999

4.5 -.71025640 25 .30000000 .10000000

9.5 -.41025640 1 .40000000 0.0000000

2.5 -.81025640 14 .40000000 .10000000

4.5 -.71025640 6 .50000000 0.0000000

11.0 -.31025641 30 .50000000 0.0000000

7.5 -.51025640 36 .50000000 .09999999

14.0 -.21025641 2 .59999999 0.0000000

1.0 -.91025640 7 .59999999 0.0000000

17.0 -.11025641 10 .59999999 0.0000000

7.5 -.51025640 31 .59999999 .10000000

14.0 -.21025641 8 .70000000 0.0000000

6.0 -.61025640 33 .70000000 0.0000000

32.0 .38974360 34 .70000000 0.0000000

21.0 .08974360 37 .70000000 .10000000

24.5 .18974359 29 .80000000 0.0000000

32.0 .38974360 32 .80000000 0.0000000

28.5 .28974359 38 .80000000 .20000000

28.5 .28974359 3 1.0000000 0.0000000

14.0 -.21025641 4 1.0000000 0.0000000

21.0 .08974360 5 1.0000000 0.0000000

28.5 .28974359 9 1.0000000 .09999999

32.0 .38974360 39 1.1000000 .80000000

38.0 .68974359 12 1.9000000

100
I1

23

4

56

7

8910

11

1213

14

15

16

17

1819

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

OBSERVATIONS ORDERED OBSERVATIONS

X(I) RANK X ( I) -MEAN WEIGHT NO. X (J) X (J+ 1 )-X (J) .4000000 18.5 -.0102564 1.000 24 -.5000000 .1000000

.5999999 24.5 .1897435 1.000 16 -.4000000 0.0000000

1.0000000 35.5 .5897436 1.000 19 -.4000000 .1000000

1.0000000 35.5 .5897436 1.000 17 -.3000000 0.0000000

1.0000000 35.5 .5897436 1.000 20 -.3000000 .1000000

.5000000 21.0 .0897436 1.000 28 -.2000000 .0999999

.5999999 24.5 .1897435 1.000 22 -.1000000 0.0000000

.7000000 28.5 .2897435 1.000 26 -.1000000 .1000000

1.0000000 35.5 .5897436 1.000 15 0 . 0.0000000

.5999999 24.5 .1897435 1.000 18 0 . .1000000

.2000000 14.0 -.2102564 1.000 21 .1000000 .1000000

1.9000000 39.0 1.4897436 1.000 11 .2000000 0.0000000

.2000000 14.0 -.2102564 1.000 13 .2000000 0.0000000

.4000000 18.5 -.0102564 1.000 23 .2000000 0.0000000

0 . 9.5 -.4102564 1.000 27 .2000000 0.0000000

-.4000000 2.5 -.8102564 1.000 35 .2000000 .0999999

-.3000000 4.5 -.7102564 1.000 25 .3000000 .1000000

0 . 9.5 -.4102564 1.000 1 .4000000 0.0000000

-.4000000 2.5 -.8102564 1.000 14 .4000000 .1000000

-.3000000 4.5 -.7102564 1.000 6 .5000000 0.0000000

.1000000 11.0 -.3102564 1.000 30 .5000000 0.0000000

-.1000000 7.5 -.5102564 1.000 36 .5000000 .0999999

.2000000 14.0 -.2102564 1.000 2 .5999999 0.0000000

-.5000000 1.0 -.9102564 1.000 7 .5999999 0.0000000

.3000000 17.0 -.1102564 1.000 10 .5999999 0.0000000

-.1000000 7.5 -.5102564 1.000 31 .5999999 .1000000

.2000000 14.0 -.2102564 1.000 8 .7000000 0.0000000

-.2000000 6.0 -.6102564 1.000 33 .7000000 0.0000000

.8000000 32.0 .3897436 1.000 34 .7000000 0.0000000

.5000000 21.0 .0897436 1.000 37 .7000000 .1000000

.5999999 24.5 .1897435 1.000 29 .8000000 0.0000000

.8000000 32.0 .3897436 1.000 32 .8000000 0.0000000

.7000000 28.5 .2897435 1.000 38 .8000000 .2000000

.7000000 28.5 .2897435 1.000 3 1.0000000 0.0000000

.2000000 14.0 -.2102564 1.000 4 1.0000000 0.0000000

.5000000 21.0 .0897436 1.000 5 1.0000000 0.0000000

.7000000 28.5 .2897435 1.000 9 1.0000000 .0999999

.8000000 32.0 .3897436 1.000 39 1.1000000 .8000000

1.1000000 38.0 .6897435 1.000 12 1.90000000

101
STATISTICAL ANALYSIS OF PIKES PEAK

NUMBER OF MEASUREMENTS = 84, NO. OF DISTINCT MEASUREMENTS = 80

FREQUENCY DISTRIBUTION WITH 10 CLASSES OF LENGTH .014670000

FREQUENCY

5 25

DISTRIBUTION

35 8100442

OF LEAST SIGNIFICANT DIGIT (0,1,..., 9)

11 10 13 8 10 4 5 8 12 3

MEASURES OF LOCATION

ARITHMETIC MEAN .63734404

MEDIAN .62915000

MID-RANGE .66844999

MID-MEAN (25 PERCENT TRIMMED MEAN) . . .62885952

MEASURES OF DISPERSION

STANDARD DEVIATION .032401812

AS PERCENT OF MEAN (COEF. OF VAR.) . 5.0838808

RANGE .14670000

MEAN DEVIATION .021074544

UTTER -QUART I LE RANGE .018249989

VARIANCE .0010498774

STANDARD DEVIATION OF MEAN .0035353275

TREND STATISTICS

SLOPE, SIGNIFICANCE LEVEL -.00024761840 .090

QUADRATIC COEFF. , SIGNIFICANCE LEVEL . 2.1859619-06 .744

OTHER TESTS FOR NON -RANDOMNESS

NUMBER OF RUNS UP AND DOWN, Z VALUE . 47 -2.267

MEAN SQUARE SUCCESSIVE DIFFERENCE . . .00036381001

MS SUCC DIFF/2 (VARIANCE) , Z VALUE . .17326309 -7.740

DEVIATIONS FROM ARITHMETIC MEAN

NUMBER OF - SIGNS, + SIGNS 62 22

NUMBER OF RUNS, Z VALUE 14 -5.550

AUTOCORRELATION COEFFICIENT .82588458

A TOO -SIDED 95 PERCENT CONFIDENCE INTERVAL FOR THE

KEAN IS .63031242 TO .64437566

MEDIAN IS .62520000 TO .63299999 (DISTRIBUTION-FREE)

S.D. IS .028026469 TO .038586477

STATISTICAL TOLERANCE INTERVAL WITH 95 PERCENT CONFIDENCE FOR

50 PCT NORMAL COVERAGE IS .61210972 TO .66257809

95 PCT NORMAL COVERAGE IS .56402038 TO .71066743

99 PCT NORMAL COVERAGE IS .54098537 TO .73370244

INTERVAL FROM MIN TO MAX HAS DIST.-FREE COVERAGE 94.48

OTHER STATISTICS

MINIMUM .59509999

SECOND MINIMUM .59519999

MAXIMUM .74180000

SECOND MAXIMUM .74070000

(MEAN -MINIMUM) /STANDARD DEVIATION . . 1.3037557

(MAXIMUM-MEAN) /STANDARD DEVIATION . . 3.2237689

SQRT(Bl) , SKEWNESS COEFFICIENT .... 1.9315909

B2 , KURTOSIS COEFFICIENT 5.9296504

LOWER QUARTILE .61987500

UPPER QUARTILE .63812499

102
Some Remarks.

(1) The distribution of least significant digits can be used to assess whether the unit

of measurement is actually what it is supposed to be. We may expect the distribution of

least significant digits to be roughly uniform. If a distance is measured to the nearest

tenth of an inch, the digits 0 to 9 after the decimal point will be roughly the same. However, if, unknown to us, the distance had been measured less precisely to the nearest one eighth of an inch with only one digit after the decimal point recorded, then the numbers 1,

4, 9 would have zero frequency. The presence of an unusually large number of zeros or fives

is reason to be suspicious and to reexamine the measurement process.

(2) A 951 distribution -free confidence interval for the median cannot be computed

exactly. Let a = 1 - 0.95 = .05 be the desired confidence coefficient. There are two

approaches taken by statisticians. We can choose the confidence interval with the largest

confidence coefficient, a*, such that a* <_ a. Or, we can choose the confidence interval

with confidence coefficient, a' closest to a. For many sample sizes the result is the same

in each case. The first case is conservative. Based upon an informal poll of NBS

statisticians, the second approach is being used in QMNITAB. The 95% confidence interval

for the median is not printed, if the number of measurements (NRMAX) is less than 5.

Brief

A BRIEF instruction suppresses all pages after the first page of the automatic printing.

See Section Cl. 1.1.

Formulas .

Let x^ be the measurements with non-zero weights and let y be the ordered measurements

with non- zero weights.

27. The z -value for the mean square successive difference is:

z = (MS Succ. Diff ,/2xVariance - l)//(2n-3-b

2 )/2n(n-l)

44. y2 45 • Vi

46 • " >’i )/s

47 * v>

/s

48 ’ [ ^-l (x

i

‘ *p

3/k]/[(k-l)s2/k] 3/2

Let r be the integral part of m. Let p

= m - r be the fractional part of m and let

q

= 1 - p.

49. Q1 = qyr

+ PYr+1>

where m = (n+l)/4

50. Q3 = pyr

+ qyr+1 , where m = 3(n+l)/4

61. Q3

- Q1 62. See section C4.5 of TN 552. Predictor is i.

63. See section C4.5 of TN 552. Predictor is i.

The correlation coefficient, r , is defined on page 158 of TN 552.

103
64 . r , where u = x. , x_ , ... , x, , and

uv I Z K“± v = x

2

, x

3

, ...

, ^

Let a be the value of c which makes

V c

( k

)2

k

closest to 0.025 L l=0 '1''

65. y' a 66 • J'k+l-a

Gau(x) is the cumulative normal distribution defined on page 167 of TN 552.

Let Pr(Gau(T )

- Gau(T) = 0.50) = 0.95

U Li 67. T = x - Ks

L w

68. Ttt = x + Ks U w

Let Pr (Gau (T^ - Gau(T

L

) = 0.95) = 0.95

69. ii *X| l 5?

70. T = X + Ks

U w

Let Pr(Gau(T ) -

JL Gau (T ) = 0.99) = 0.95Li 71. Tt = x - Ks

L w

72. T, = x + Ks

U w

73. The largest value! of p such that

kp

k 1

- (k-l)p

k <0.05

4.5* Analysis Of Groups Of Data .

Many improvements have been made to the printing performed by a ONEWAY instruction.

General improvements are:

(i) All printing has been revised to use 72 characters per line instead of 120.

lienee, ONEWAY can now be used interactively with a narrow width carriage terminal or CRT

display device.

(ii) All printing with scientific notation has been changed to readable printing with

a constant number of significant digits with the decimal points lined up.

(iii) The restrictions on page 122 of NBS TN 552 marked by an asterisk have finally

been lifted. Group numbers do not have to be (a) consecutive integers, (b) integers, or (c)

positive.

Specific changes are:

(i) The addition of a two -line title at the beginning which gives the number of

measurements and column heading on the first line and the number of groups and the column

heading for the column containing the group numbers on the second line. If a LABEL or HEAD

instruction has been used, its effect is reflected in the column heading used in this twoline title.

104
(ii) The printing is divided into six sections: analysis of variance, estimates,

pairwise multiple comparison of means, tests for homogeneity of variances, Model II - components of variance, and a new section showing box plots. The title of each section is

enclosed in a box.

(iii) When lines are printed in the analysis of variance for "SLOPE" and "ABOUT LINE"

(formerly DEVS. ABOUT LINE) the SUMS OF SQUARES, MEAN SQUARES and PROB. (or significance

level) are not indented, as before, because of space limitations. The H-statistic for the

Kruskal -Wallis rank test is no longer printed, since the significance level contains all the

information in the data.

(iv) In the printing of the estimates, the use of H and L to pinpoint the high and low

values in a column has been extended to the SUM RANKS, MINIMUM, MAXIMUM, and S.D. OF MEAN

columns. The blank line at the bottom of the column has been replaced by a line with dashes

suitably placed. Because of space restrictions, the estimates section has been divided into

two subsections. If the number of groups exceeds 14, the second subsection starts on a new

page.

Box Plots

A new section has been added, which shows box plots for each group. A box plot is a graphical display of the scrawl of a set of measurements, where the scrawT consists of the

five statistics: the minimum, the lower hinge, the median, the upper hinge, and the

maximum. (See Section C4.1.1.) The lower hinge and the upper hinge are essentially

equivalent to the lower and upper quartiles.

An examination of a box plot enables one to get a quick assessment of the shape of the

distribution of the numbers; including an assessment of location, variation and skewness.

By examining all of the box plots simultaneously, the relative shapes of the distributions

for the different groups can be assessed. There are several variations of box plots which

are described in McGILL et al. (1977). The simplest form is given by ONEWAY.

A box plot has a line starting with an asterisk to represent the minimum and ending with

an asterisk to represent the maximum. In between, H's are appropriately placed to represent

the hinges and M is appropriately placed inside to represent the median. A box is erected

above and below the line from the lower hinge to the upper hinge.

In small samples, a hinge, the median, or an extreme value may coincide. In such cases, only one symbol is printed. The M, for median, takes precedence over an H, for hinge, or an

*, for an extreme value, and an * takes precedence over an H.

Obviously, at least five measurements are needed to print a box plot. If a group has

less than five measurements, an X is used to represent each measurement in the group. If

two or more points coincide, a single X is printed.

The box plot section starts on a new page. A title and axis are printed at the top of

the page and on any subsequent pages. Three numbers are printed above the 0's of the axis

to show the scale; namely, the minimum, the midrange and the maximum of all the

measurements. The group number and the number of measurements in the group are printed on

the left.

105
Remark.

There are times when a zero group number is appropriate, as in temperatures of -20, -10,

0, 10, 20 and 30 degrees. There are times when it is desirable to delete one or more

observations from an analysis by assigning a zero weight. These requirements are in

conflict, unless the structure of the instruction is complicated by the addition of another

argument

.

Since several editing instructions were added to OMNITAB II, it has been decided to make

zero a valid group number. If certain observations are to be deleted from an analysis, it can be easily done using one of the DELETE instructions described in Section C3.5.1.

Suppose an analysis is needed without using the data in rows 3 and 7. Let the measurements

be in column 11 and the group numbers in column 12. Pick any number, such as -99.99, which

is not being used as a group number. Now the following set of instructions can be used:

DEFINE -99.99 into row 3 of column 12

DEFINE -99.99 into row 7 of column 12

DELETE rows with -99.99 in cols 11 and 12 and put in cols 13 and 14

ONEWAY analysis of column 13 with group numbers in column 14

Remark .

If a set of instructions is rerun using the latest Omnitab II version, some differences

may be observed in the numerical results which will be caused by the use of more sophisticated algorithms and substantially better programming. The results will be more

accurate

.

Examples .

Pages 107, 108 and 109 show the printing for the instructions

LABEL GRAVITY G

$

SET measurements in GRAVITY G

83 81 76 78 79 72 61 61 67 67 64 78 71 75 72 74

$

SET group numbers in column 2 1111112222233333

$

ONEWAY analysis of GRAVITY G with group numbers in column 2

The data is the same as that used to produce page 126 of NBS TN 552; except a LABEL

instruction has been used to produce a column heading on the first line of the printing.

106
OMNITAB 1/15/77 EXAMPLE OF ONEWAY - BROWNLEE DATA PAGE 251 PG 1

ONEWAY ANALYSIS OF 16 MEASUREMENTS IN GRAVITY G

CLASSIFIED INTO 3 GROUPS WITH NUMBERS IN COLUMN 2

I ANALYSIS OF VARIANCE I

SOURCE D.F. SUMS OF SQUARES MEAN SQUARES F RATIO PROB.

BETWEEN GROUPS 2 565.10412 282.55206 26.082 .000

SLOPE 1 64.508909 64.508909 1.408 .257

ABOUT LINE 1 500.59521 500.59521 46.209 .000

WITHIN GROUPS 13 140.83333 10.833333

TOTAL 15 705.93750

KRUSKAL -WALLIS RANK TEST FOR DIFFERENCE BETWEEN MEANS

SIGNIFICANCE LEVEL IS APPROXIMATELY .000

I ESTIMATES I

GROUP NO. MEAN MINIMUM MAXIMUM SUM RANKS

1 6

2 5 3 5

78.166666 H

64.000000 L

74.000000

72.000000 H

61.000000 L 71.000000

83.000000

67.000000

78.000000

H 76. OH

L 15. 0L

45.0

TOTAL 16 72.437500 61.000000 83.000000

GROUP WITHIN S.D. S.D. OF MEAN 95 PCT CONF INT FOR MEAN

1

23 MODEL

FIXED

RANDOM

UNGROUPED

3.8686776 H

3.0000000

2.7386128 L

3.2914029

7.2957575

6.8602114

1.5793810 H

1.3416408

1.2247449 L

.82285073

4.2122076

1.7150528

74.106800 TO 82.226532

60.275069 TO 67.724931

70.599619 TO 77.400381

70.659840 TO 74.215160

54.313635 TO 90.561365

68.781952 TO 76.093048

107
OMNITAB 1/15/77 EXAMPLE OF ONEWAY - BROWNLEE DATA PAGE 251 PG 2

I PAIRWISE MULTIPLE COMPARISON OF MEANS I

THE MEANS ARE PUT IN INCREASING ORDER IN GROUPS SEPARATED BY ***. A

MEAN IS ADJUDGED NON-SIGNIFICANTLY DIFFERENT FROM ANY MEAN IN THE SAME

GROUP AND SIGNIFICANTLY DIFFERENT AT THE .05 LEVEL FROM ANY MEAN IN

ANOTHER GROUP. ***** INDICATES ADJACENT GROUPS HAVE NO COMMON MEAN.

- NEWMAN- KEULS TECHNIQUE, HARTLEY MODIFICATION - (APPROXIMATE, IF THE NUMBERS OF MEASUREMENTS IN THE GROUPS DIFFER)

64.000000

***** 74.000000, 78.166666

SCHEFFE TECHNIQUE

64.000000

***** 74.000000, 78.166666

I TESTS FOR HOMOGENEITY OF VARIANCES I

COCHRAN'S C = MAX VARIANCE/SUM (VARIANCES) = .4756, APPROX SL = .439

BARTLETT-BOX F = .269, SIGNIFICANCE LEVEL = .764

MAXIMUM VARIANCE / MINIMUM VARIANCE = 1.9955554

I MODEL II - COMPONENTS OF VARIANCE I

ESTIMATE OF BETWEEN COMPONENT OF VARIATION IS 51.147054

108
OMNITAB 1/15/77 EXAMPLE OF ONEWAY - BROWNLEE DATA PAGE 251 PC 3

I BOX PLOTS I

61.000000 72.000000 83.000000

GROUP NO. O + 1 + 1 O 1 + 1 + O

I I 1 6

* H M H

*

I I

I I 2 5 * M *

I I

I I 3 5 *-H M H

I I

109
Pages 111, 112, 113, 114 and 115 show the printing for an analysis of some 1976 EPA

mileage data published in the Washington Post which was produced by the following set of

instructions:

OMNITAB 1/12/77 USE ONEWAY WITH EPA76 DATA

DIMENSION worksheet to be 400 rows x 30 columns

WIDTH 72 LABEL COMBINED MPG,3, ENGINE SIZE, 15

SET

33

mileage

33 33

measurements

32 32 30

in

30

COMBINED MPG

29 29 29 29 29 29 29 29 29 28 28 28 28

28 28 28 28 28 27 27 27 27 27 27 27 27 26 26 26 26 26 26 26

26 26 26 26 26 26 26 26 26 26 25 25 25 25 25 25 25 25 25 25

25 25 25 24 24 24 24 24 24 24 24 24 24 24 24 23 23 23 23 23

23 23 23 23 23 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22

22 22 22 22 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21

21 21 21 21 20 20 20 20 20 20 20 20 20 20 20 20 22 20 20 20

20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 22

19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 18 18 18 18 18 18 18 18 18 18 18 18

18 18 18 18 18 18 18 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17

16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15 15 16 16

15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15

15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14

14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

13 13 13 13 13 13 13 13 12 12 12 12 12 12 12 12 12 12 12 12 SET

98

measurements

85 83 85

; in

79

ENGINE SIZE

129 129 97 97 91 98 85 73 91 97 97 97 97 140 97

97 97 97 97 97 140 119 119 119 140 140 140 140 85 140 140 119 140 140 140

140 140 140 140 140 140 140 97 97 97 140 140 140 119 119 119 79 200 200 200

200 140 140 119 79 79 120 120 133 133 133 133 133 133 122 258 258 258 114 140

140 140 140 340 140 231 140 140 140 226 225 225 225 107 107 231 225 225 225 140

140 231 140 122 232 258 232 231 250 225 107 107 250 171 140 140 141 171 250 231

225 225 231 152 232 232 232 114 231 231 250 250 250 250 122 168 225 107 107 250

200 171 200 250 250 250 260 225 250 250 250 250 250 156 156 130 130 130 134 140

258 258 232 258 232 231 260 231 168 225 225 250 171 171 171 250 260 250 260 260

225 225 250 260 260 260 163 163 304 304 262 318 318 318 250 302 302 250 260 318

318 318 350 400 260 163 163 258 258 350 350 350 350 305 305 305 305 305 318 318

318 318 302 302 302 30 302 350 350 350 318 318 318 318 400 400 350 350 156 156

304 304 350 350 350 350 351 302 302 351 455 350 400 350 455 400 304 351 350 350

350 350 400 350 350 400 350 350 400 400 350 350 318 360 318 360 318 351 351 351

400 351 351 351 351 455 350 455 350 455 318 318 360 360 400 400 455 455 400 400

304 360 304 360 455 455 455 350 400 360 400 360 360 360 400 400 360 400 360 400

400 400 400 400 400 400 400 400 455 455 455 360 360 400 400 360 360 400 455 164

360 360 455 500 500 454 454 440 400 400 400 440 400 460 460 460 460 460 460 460

460 460 460 460 460 400 400 400 401 401 500 500 440 440 440 400 326 412 412 412

CODE column 15 with length 25.0 and put in column 11

MINIMUM value of column 15 put in column 12 MULTIPLY column 11 by 25.0 and put in column 13

ADD columns 12 and 13 and put in column 14

SUBTRACT 12.5 from column 14 and put group number in column 15

ONEWAY analysis of column 3 using group numbers in column 15

110
OMNITAB 1/12/77 USE ONEWAY WITH EPA76 DATA PG 1

ONEWAY ANALYSIS OF 380 MEASUREMENTS IN COMBINED MPG

CLASSIFIED INTO 19 GROUPS WITH NUMBERS IN ENGINE SIZE

I ANALYSIS OF VARIANCE I

SOURCE D.F. SUMS OF SQUARES MEAN SQUARES F RATIO PROB.

BETIVEEN GROUPS 18 7945.3173 441.40652 157.976 .000

SLOPE 1 7047.1467 7047.1467 ******* .000

ABOUT LINE 17 898.17059 52.833564 18.909 .000

WITHIN GROUPS 361 1008.6827 2.7941350

TOTAL 379 8954.0000

KRUSKAL -WALLIS RANK TEST FOR DIFFERENCE BETWEEN MEANS

SIGNIFICANCE LEVEL IS APPROXIMATELY .000

I ESTIMATES I

GROUP NO. MEAN MINIMUM MAXIMUM SUM RANKS

42.5 1 17.000000 17.000000 17.000000 157.0

67.5 5 26.800000 24.000000 32.000000 1692.5

92.5 24 28.791667 H 26.000000 H 33.000000 H 8701.5

117.5 23 23.869565 20.000000 30.000000 6995.0

142.5 50 24.000000 20.000000 28.000000 15480. 5H

167.5 17 18.823529 14.000000 21.000000 3458.5

192.5 6 23 . 353333 20.000000 25.000000 1771.0

217.5 16 20.937500 19.000000 22.000000 4137.0

242.5 39 20.051282 18.000000 22.000000 9264.0

267.5 20 19.400000 17.000000 23.000000 4331.5

292.5 15 16.466666 14.000000 18.000000 2108.5

317.5 25 16.640000 12.000000 L 18.000000 3653.0

342.5 39 15.769231 14.000000 18.000000 4798.5

367.5 18 14.111111 13.000000 15.000000 1185.0

392.5 41 14.243902 12.000000 L 18.000000 2907.0

417.5 3 12.000000 L 12.000000 L 12.000000 L 19. 5L

442.5 7 12.571429 12.000000 L 13.000000 125.5

467.5 27 13.851852 13.000000 16.000000 1538.5

492.5 4 12.500000 12.000000 L 13.000000 66.0

TOTAL 380 19.000000 12.000000 33.000000

111
OMNITAB 1/12/77 USE ONEWAY WITH EPA76 DATA PG 2

GROUP WITHIN S.D. S.D. OF MEAN 95 PCT CONF INT FOR MEAN

42.5 *** ESTIMATES ARE NOT AVAILABLE BECAUSE SAMPLE SIZE IS 1 *** 67.5 3.5637060 H 1.5937378 H 22.375147 TO 31.224853

92.5 2.0847097 .42553958 27.911371 TO 29.671962

117.5 3.0196982 .62965059 22.563750 TO 25.175380

142.5 2.2497165 .31815796 23.360638 TO 24.639362

167.5 1.7042335 .41333732 17.947294 TO 19.699765

192.5 2.5819889 1.0540925 20.623744 TO 26.042925

217.5 1.2893797 .32234492 20.250438 TO 21.624562

242.5 .97194102 .15563512 19.736215 TO 20.366349

267.5 1.7888544 .39999999 18.562790 TO 20.237209

292.5 1.3557637 .35005668 15.715870 TO 17.217463

317.5 1.4106736 .28213472 16.057703 TO 17.222297

342.5 .93080440 .14904799 15.467499 TO 16.070962

367.5 .58298308 .13741043 13.821200 TO 14.401022

392.5 1.2996247 .20296728 13.833690 TO 14.654114

417.5 0 . L 0 . L 12.000000 TO 12.000000

442.5 .53452248 .20203051 12.077080 TO 13.065777

467.5 .98853831 .19024429 13.460799 TO 14.242904

492.5

MODEL

.57735027 .28867513 11.581320 TO 13.418680

FIXED 1.6715666 .085749559 18.831369 TO 19.168631

RANDOM 5.0623025 1.1613718 16.560049 TO 21.439951

UNGROUPED 4.8605894 .24934298 18.509731 TO 19.490269

112
OMNITAB 1/12/77 USE ONEWAY WITH EPA76 DATA PG 3

I PAIRWISE MULTIPLE COMPARISON OF MEANS I

THE MEANS ARE PUT IN INCREASING ORDER IN GROUPS SEPARATED BY ***. A

MEAN IS ADJUDGED NON-SIGNIFICANTLY DIFFERENT FROM ANY MEAN IN THE SAME

GROUP AND SIGNIFICANTLY DIFFERENT AT THE .05 LEVEL FROM ANY MEAN IN ANOTHER GROUP. ***** INDICATES ADJACENT GROUPS HAVE NO COMMON MEAN.

NEWMAN-KEULS TECHNIQUE, HARTLEY MODIFICATION

(APPROXIMATE, IF THE NUMBERS OF MEASUREMENTS IN THE GROUPS DIFFER)

12.000000,

15.769231,

*****

12.500000,

16.466666,

12.571429,

16.640000,

13.851852,

17.000000

14.111111,

18.823529,

*** 19.400000

19.400000,

*** 20.051282

20.051282,

***** 20.937500

23.333333,

***** 23.869565, 24.000000

26.800000

***** 28.791667

- SCHEFFE TECHNIQUE -

12.000000,

15.769231,

20.051282,

***

12.500000,

16.466666,

20.937500

12.571429,

16.640000,

13.851852,

17.000000,

14.111111,

18.823529,

18.823529,

*** 19.400000, 20.051282, 20.937500, 23.333333,

20.937500,

*** 23.333333, 23.869565, 24.000000

23.869565, 24.000000, 26.800000, 28.791667

I TESTS FOR HOMOGENEITY OF VARIANCES I

COCHRAN'S C = MAX VARIANCE/SUM (VARIANCES) = .2312, APPROX SL = .000

BARTLETT -BOX F = 7.660, SIGNIFICANCE LEVEL = .000

MAXIMUM VARIANCE / MINIMUM VARIANCE = 0

.

ASSUMING HETROGENEOUS VARIANCE, APPROX. BETWEEN MEANS F-TEST SL = .000

I MODEL II - COMPONENTS OF VARIANCE I

ESTIMATE OF BETWEEN COMPONENT OF VARIATION IS 22.542293

113
OMNITAB 1/12/77 USE ONEWAY WITH EPA76 DATA PG 4

I BOX PLOTS I

12.000000

GROUP NO. 0 + -- 22.500000 33.000000--I + x o 1 + 1 + O

42.5 1 X

67.5 5

I I * M H

*

I I

92.5 24

I I * HMH *

I I

117.5 23

I I *-H M H

*

I I

142.5 50

I I * H M H

*

I I

167.5 17

I I H M H-*

I I

192.5 6

I I * M

I I

217.5 16

I I *H M*

I I

242.5 39

I I *--H M H--*

I I

114
OMNITAB 1/12/77 USE ONEWAY WITH EPA76 DATA PG 5

GROUP

12.000000 22.500000 33.000000

NO. 0 + 1 + 1 0 1 + 1 + 0

267.5

I I 20 *--H MH *

I I

292.5

I I 15 * H M

*

I I

317.5

I 25 * -M-*

I

342.5

I I 39 *-H M H-*

I I

367.5

I 18 *—M-*

I

392.5

I I 41 * MH *

I I

417.5 3 X

442.5

I I 7 * M

I I

467.5

I I 27 * M H—

I I

492.5 4 X X

115
4.4* Analysis Of Two-way Table .

Some minor changes have been made in the printing produced by a TWOWAY instruction.

Some examples are given in section C4.4.1.

4.4.1 Twoway Fixed Effects Design With Replication .

A TWOWAY instruction can be used to analyze data from a row by column classification

with one or no measurements in each cell. If there are more than one measurement in any

cell, a TWOWAY instruction cannot be used directly. However, it can be used indirectly, as

is explained below. The method outlined is practical, but would be improved substantially

by a specific instruction which may be added in the future.

The analysis of variance table for a two-way classification with replication is:

Source d.f. Sums of Squares Mean Squares F Ratio Significance

Rows DR SR MR FR PR

Columns DC SC MC FC PC

Row x Column Interaction DI SI MI FI PI Residual DE SE ME

Total DT ST

The above table can be easily constructed by hand after using a set of OMNITAB

instructions to obtain the necessary sums of squares and mean squares.

The degrees of freedom are calculated as follows:

DR = r-1, where r = number of rows

DC = c-1, where c = number of columns

DI = (r-1) (c-1)

DE = SUM (n^-1), where n^ is the number of measurements

in the ith cell and i=l, 2, ..., rc

DT = SUM(n )-l

The sums of squares can be calculated in a number of w

rays . The following procedure is

suggested. Enter the measurements into the worksheet using a SET instruction. Using

another SET instruction, enter group numbers for a ONEWAY analysis. Set the group number

equal to 1 for the measurements in the first row of the first column of the rxc table, 2 for

the measurements in the first row of the second column, etc., until numbers for the first row have been assigned. Continue, using consecutive integers, for the second roAv, third

row, etc. Do a ONEWAY analysis using the form of ONEWAY with six arguments for storing the

group number, number of measurements in each group, the means and the standard deviations.

Then do a TWOWAY analysis using the means from ONEWAY as measurements and the numbers in the

groups as the weights.

The sums of squares for the analysis of variance are obtained as follows:

SR = row sum of squares from TWOWAY

SC = column sum of squares from TWOWAY

SI = residual sum of squares from TWOWAY

SE = within sum of squares from ONEWAY

ST = total sum of squares from ONEWAY

116
Note, the row and column sums of squares from TWOWAY should be chosen from the line showing

a value for F PROB. Note that SR + SC + SI + SE will not equal ST, unless there is an equal

number of measurements in each cell or the cell frequencies are proportional.

The mean squares are obtained, as usual, by dividing the sums of squares by the

corresponding degrees of freedom. For a fixed effects model, the F-ratios FR, FC and FI are obtained by dividing MR, MC and MI by ME.

An F-test should be done to assess whether there is a significant row by column

interaction. If there is a significant interaction, it makes little sense to test for row

or column effects. Instead, a graphical study of the means should be done to determine the reason for the row by column interaction. A significant interaction may be caused by an outlier or an inappropriate scale for the response measurements. If an outlier is present,

an attempt should be made to determine the cause. A reanalysis without using the outlier

may be appropriate. If an outlier is not present, a transformation (such as log or square

root) of the response variable may remove the interaction. Probability plots of residuals

may be helpful.

Note, the BETWEEN GROUPS sum of squares from the ONEWAY analysis should equal the TOTAL

sum of squares from the TWOWAY analysis.

Example .

The following data has been drawn from a medical thermometry experiment. The data

consisted of measurements of bead thermistor drift rates for five manufacturers and three

resistances: low, medium and high. A set of OMNITAB instructions to analyze the data is:

OMNITAB 2/16/77 ANALYSIS OF MEDICAL THERMOMETRY DRIFT RATES

WIDTH 80

SET DRIFT RATE IN COLUMN 1 -.00146271 .02112775 .00282039 -.02015703 -.00576177 .00087166 .00195146

.00486606 .00023100 -.03231597 -.05563992 .01034201 -.00423480 -.00661010

-.01153926 .00618470 .00389811 .01307638 .00128444 .00779145 .01144474

.00788975 -.02578416 .00356302 .01287440 -.05037265 -.00086367 -.00673810

.00921573 .00604309 .00219059 .01001163 .01505623 .00875096 .00931758

-.00713043 .02330194 -.00255854 .02775204 .01250641 .00207295 -.00672891

.00517192 -.00761442 .03754005 .00952288 .00981737 .00480323 .02078291

.01174765 -.22716029 -.01801598 .01118366 .00146129 .02934898 .01127789

-.05363813 .00084837

SET ROW-COLUMN CELL NUMBER IN COLUMN 2 11111222233444 44555556666677 778888899999 10 10

10 10 10 11 11 11 11 11 12 12 12 15 15 15 15 15

ONEWAY ANALYSIS OF COLUMN 1 WITH IDENTIFICATION IN COLUMN 2 STORE 11 12 13 14

DEFINE 13,12 INTO 15,12

DEFINE 13,13 INTO 15,13

ADEFINE 13,12 SIZE 2X2 EQUAL TO 0.0

RESET 15 TWOWAY ANALYSIS FOR 5X3 TABLE OF MEANS IN COL 13 STORE 21 WEIGHTS 12

The results are shown on pages 118 to 123.
OMNITAB 2/16/77 ANALYSIS OF MEDICAL THERMOMETRY DRIFT RATES PG 1

ONEWAY ANALYSIS OF 58 MEASUREMENTS IN COLUMN 1 CUVSSIFIED INTO 13 GROUPS WITH NUMBERS IN COLUMN 2

I ANALYSIS OF VARIANCE I

SOURCE D.F. SUMS OF SQUARES FEAN SQUARES F RATIO PROB.

BETWEEN GROUPS 12 .024407991 .0020339992 2.041 .042

SLOPE 1 .00022141700 .00022141700 .180 .674

ABOUT LINE 11 .024186574 .0021987795 2.207 .027

WITHIN GROUPS 45 .044841114 .00099646919

TOTAL- 57 .069249106

KRUSKAL - WALLI S RANK TEST FOR DIFFERENCE BETWEEN MEANS

SIGNIFICANCE LEVEL IS APPROXIMATELY .205

I ESTIMATES I

GROUP NO. MEAN MINIMUM MAXIMUM SUM RANKS

1 5 -.00068667396 -.020157030 .021127750 122.0

2 4 .0019800450 .00023100000 .0048660600 99.0

3 2 -.043977945 -.055639920 -.032315970 L 7.0L

4 5 -.0011714900 -.011539260 .010342010 118.0

5 5 .0074990239 .0012844400 .013076380 187.0

6 5 -.010365928 -.050372650 .012874400 126.0

7 4 .0019142625 -.0067381000 .0092157299 104.0

8 5 .0090653979 .0021905900 .015056230 200.0

9 5 .010774284 -.0071304300 .027752040 188.0

10 5 .0060883179 -.0076144200 .037540050 H 140.0

11 5 .011334808 H .0048032300 H .020782910 215. OH

12 3 -.077997536 L -.22716029 L .011183660 54.0

15 5 -.0021403202 -.053638130 .029348980 151.0

TOTAL 58 -.0026617961 -.22716029 .037540050

GROUP WITHIN S.D. S.D. OF MEAN 95 PCT CONF INT FOR MEAN

1 .014946757 .0066843929 -.019245222 TO .017871874

2 .0020508190 L .0010254095 L -.0012832181 TO ,.0052433081

3 .016492523 .011661975 -.19215732 TO .10420144

4 .0091258864 .0040812204 -.012502590 TO .010159610

5 .0049585463 .0022175293 .0013422756 TO ,.013655772

6 .026952077 .012053335 -.043830808 TO ,.023098952

7 .0071400029 .0035700015 -.0094469101 TO .013275435

8 .0045883242 .0020519609 .0033683336 TO ,.014762462

0 .015382860 .0068794243 -.0083257495 TO .029874317

10 .018427023 .0082408153 -.016791482 TO ,.028968117

11 .0058669472 .0026237785 .0040501492 TO ..018619466

12 .13000115 H .075056201 H -.40094185 TO ,.24494678

15

MODEL

.031006260 .013866421 -.040639051 TO ..036358411

FIXED .031566900 .0041449370 -.011010128 TO ..0056865356

RANDOM .026115926 .0072432547 -.018443490 TO ..013119898

UNGROUPED .034855367 .0045767339 -.011826547 TO ..0065029552

118
OMNITAB 2/16/77 ANALYSIS OF MEDICAL THERMOMETRY DRIFT RATES PG 2

I PAIRWISE MULTIPLE COMPARISON OF MEANS I

THE MEANS ARE PUT IN INCREASING ORDER IN GROUPS SEPARATED BY ***. A

MEAN IS ADJUDGED NON-SIGNIFICANTLY DIFFERENT FROM ANY MEAN IN THE SAME

GROUP AND SIGNIFICANTLY DIFFERENT AT THE .05 LEVEL FROM ANY MEAN IN ANOTHER GROUP. ***** INDICATES ADJACENT GROUPS HAVE NO COMMON MEAN.

NEWMAN-KEULS TECHNIQUE , HARTLEY MODIFICATION - (APPROXIMATE, IF THE NUMBERS OF MEASUREMENTS IN THE GROUPS DIFFER)

-.077997536, -.043977945

-.043977945, -.010365928, -.0021403202, -.0011714900, -.00068667396,

.0019800450, .0060883179, .0074990239, .0090653979, .010774284,

SCHEFFE TECHNIQUE

-.077997536, -.043977945, -.010365928, -.0021403202, -.0011714900,

.0019142625, .0019800450, .0060883179, .0074990239, .0090653979,

.011334808

I TESTS FOR HOMOGENEITY OF VARIANCES I

COCHRAN'S C = MAX VARIANCE/SUM(VARIANCES) = .8502, APPROX SL = .000

BARTLETT-BOX F = 7.386, SIGNIFICANCE LEVEL = .000

MAXIMUM VARIANCE / MINIMUM VARIANCE = 4018.2760

ASSUMING HETROGENEOUS VARIANCE, APPROX. BETWEEN MEANS F-TEST SL = .000

I MODEL II - COMPONENTS OF VARIANCE I

ESTIMATE OF BETWEEN COMPONENT OF VARIATION IS .00023339397

119
OMNITAB 2/16/77 ANALYSIS OF MEDICAL THERMOMETRY DRIFT RATES PG 3

I BOX PLOTS I

-.22716029

GROUP NO. 0 +-- -.094810121 .037540050--I + 1 O 1 + 1 + 0

1 5 II *—HM—

II

2 4 XX

3 2 X X

4 5

I I *M H*

I I

5 5 II *M*

II

6 5

I I * H M*

I I

7 4 xxxx

8 5

I

I

9 5

I I * M H*

I I

10 S

I I *HMH *

I I

120
OMNITAB 2/16/77 ANALYSIS OF MEDICAL THERMOMETRY DRIFT RATES PG 4

-.22716029 -.094810121

GROUP NO. 0 + 1 + 1 0 1 + 1 +-

I 11 5 *MI

12 3 X X X

I I 15 5

* M H

I I

037540050

---0

*

121
OMNITAB 2/16/77 ANALYSIS OF MEDICAL THERMOMETRY DRIFT RATES PG 5

ANALYSIS OF VARIANCE FOR TWO-WAY 5X3 TABLE

SOURCE D.F. SUMS OF SQUARES MEAN SQUARES F RATIO

BETWEEN ROWS 4 .0054699485 .0013674871 .646

BETWEEN COLS 2 .0062415992 .0031207996

RESIDUALS 6 .012696442 .0021160737

TOTAL 12 .024407992

BETWEEN ROWS 4 .0027475457 .00068688644

BETWEEN COLS 2 .0089640018 .0044820009 2.118

RESIDUALS 6 .012696442 .0021160737

TOTAL 12 .024407992

A WEIGHTED LEAST SQUARES ANALYSIS USING

13 NON-ZERO WEIGHTS AND 2 ZERO WEIGHTS IN COLUMN 12

COEFFICIENT ESTIMATE STD. DEV.

GRAND MEAN -.00042717298 .0066603256

ROW 1 -.011283253 .013046438

ROW 2 -.00091895890 .011357944

ROW 3 .0085589782 .011603147

ROW 4 -.013821500 .012155542

ROW 5 .017464733 .019091041

COLUMN 1 .0069898283 .0088861858

COLUMN 2 .012188053 .0088157885

COLUMN 3 -.019177881 .0094682242

RESIDUAL .046000801

F PROB.

.650

.201

122
OMNITAB 2/16/77 ANALYSIS OF MEDICAL THERMOMETRY DRIFT RATES PG 6 5X3 TABLE OF RESIDUALS, STANDARDIZED BY

DIVIDING EACH RESIDUAL BY ITS STANDARD DEVIATION.

COLUMN 12 3 ROW

1 .30 .09 1 OO

2

1 00 -.23 .73

3

1 oo l OO o 1.59

4 .96 .96 -2.17

5 .00 .00 O O

123
STANDARDIZED

RESIDUALS

VS

ROW

NUMBER

STANDARDIZED

RESIDUALS

VS

PREDICTED

RESPONSE

3.75 +++X+++

3.75 +++X++

CM + O

124 I

00+0000

'T

0000

‘0

00+0000' IS2'£- +++X+++

S2‘£-
An analysis of variance for these data is:

Source d.f

.

Sums of Squares Mean Squares F Ratio

Manufacturers (Rows) 4 .0054699485 .0013674871

Resistance (Cols) 2 .0062415992 .0031207996

Interaction 6 .012696442 .0021160737 2.12

Residual 45 .044841114 .00099646919

Total 57 .069249106

Note, that no measurements were available for the fourth manufacturer for low and medium

resistance. Hence, two degrees of freedom are lost for estimating the manufacturers x

resistance interaction.

After an examination of the ONEWAY analysis, in this particular example, one might

decide not to proceed with a TWOWAY analysis . It is clear from the box plots that one of

the high resistance measurements for the fourth manufacturer is substantially smaller than

any of the other measurements . An attempt should be made to determine the cause of this low

measurement before proceeding with any further analysis

.

The interaction F-ratio is almost significant at the 0.05 level. No sophisticated

analysis is needed, as the interaction is undoubtedly due to the outlier detected by an

examination of the box plots.

It is a good idea to examine plots of the residuals. The instruction:

FIT 13, 12, 7, 2 1***2

7

will give the four plots of residuals shown on page 124.

4.5* Regression .

Minor changes have been made in the printing of the FIT and POLYFIT instructions for

batch use. Columns three and four of the analysis of variance have been deleted (CUM. MS

REDUCTION and D.F.). Titles have been changed slightly. In some cases, columns titled

COLUMN are now titled INDEP. VAR. and the numbers below replaced by COLUMN xxxx, where xxxx

is the column number, or the heading if a LABEL or HEAD instruction has been used. Pages

127 to 130 show the results of using the set of instructions at the bottom of page 139 of

NBS TN 552 and, in effect, replace pages 140 to 143 of NBS TN 552.

The printing has been substantially revised for interactive use or use after a WIDTH

instruction with an argument less than 120 so that all printing uses 72 characters per line.

Minor changes have been made in the first page. Two pages are used instead of one for the

four plots of standardized residuals. The analysis of variance is split into two sections

and one is printed below the other. At the end the printing for the fit omitting the last

independent variable (term) appears below, instead of on the right of, the printing of the

coefficients and their standard deviations.

125
Pages 131 to 135 show the printing for the example on page 139 of NBS TN 552 using:

WIDTH 80

POLYFIT COL 1 WTS 1.0, DEGREE 1 WITH X IN COL 3

instead of the instructions

DEFINE 1.0 INTO COLUMN 2 FIT COL 1 WTS 1.0, 2 VEC'S IN COLS 2 and 3

Note, on the first page of printing (page 131) the weights are not printed on the right.

Values of the independent variable are printed on the right where the weights are normally

printed. However, if the second argument is a column number or a constant not equal to one, the weights are printed instead of values of the independent variable.

Brief

If a BRIEF instruction is used:

(a) the first page is not printed

.

(b) the last two plots of standardized residuals are not printed (lower half of

page with normal printing)

.

(c) the variance -covariance matrix is not printed and the second half of the

analysis of variance is not printed.

(d) the printing for the fit omitting the last term is omitted.

Pages 136 to 138 show the results of using the instructions

BRIEF

WIDTH 80

POLYFIT COL 1 WTS 1.0, DEGREE 1 WITH X IN COL 3

with the example, already discussed, on page 139 of NBS TN 552.

After we have had more experience, the action taken by BRIEF may be modified. An

optional form may be added with an argument, which works something like LIST (n) to

selectively delete parts of the printing.

126
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fO o o o o o o o o o o o o o o o o o o o o o o c o o o

LO LO LO LO LO ro LO o N

Csl to *3- LO

127
STANDARDIZED

RESIDUALS

VS

ROW

NUMBER

STANDARDIZED

RESIDUALS

VS

PREDICTED

RESPONSE

128

3.5000+01

5.5000+01

7.5000+01
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130
OMNITAB 6/9/69 FIT EXAMPLE FROM SNEDECOR-COCHRAN PP 135-140 14

LEAST SQUARES FIT OF RESPONSE, COLUMN 1,

AS A POLYNOMIAL OF DEGREE 1. INDEPENDENT VARIABLE IS COLUMN 3 USING 5 NON-ZERO WEIGHTS = 1.0000000

RESPONSE PREDICTED STD. DEV. OF STD. INDEP VAR. ROW COLUMN 1 RESPONSE PRED. RESPONSE RES. COLUMN 3

1 114.00000 113.40000 2.5139609 .29 35.000000

2 124.00000 127.20000 1.7776388 -1.18 45.000000

3 143.00000 141.00000 1.4514360 .69 55.000000

4 158.00000 154.80000 1.7776388 1.18 65.000000

5 166.00000 168.60000 2.5139609 -1.27 75.000000

131
OMNITAB 6/9/69 FIT EXAMPLE FROM SNEDECOR-COCHRAN PP 135-140 15

3.75 +-- STANDARDIZED RESIDUALS VS ROW NUMBER

+ + x + + +

2.25+ +

.75+

*

* +

_ a

-.75+ +

* A _

2.25+ +

3.75 +--

1.0 3.0000 5.0

3.75 +-- STANDARDIZED RESIDUALS VS PREDICTED RESPONSE

+ + x + + +

2.25+ +

.75+

A

* +

_ A

-.75+ +

* *_

2.25+ +

3.75 +-- + + x + + + 1.1340+02 1.4100+02 1.6860+02

132
STANDARDIZED RESIDUALS VS COLUMN 3 3.75 +-- +--- -+ X + --- + +

2.25+ +

.75+

_*

*

* +

-.75+ +

* *_

2.25+ +

3.75 + 3.5000+01

---+ + x + + + 5.5000+01 7.5000+01

NORMAL PROBABILITY PLOT OF STANDARDIZED RESIDUALS

3.75 + + + X + + +

2.25+ +

.75+

*

* +

*

-.75+ +

*

*

2.25+ +

3.75 + -2.5

--- + + x + + + 0.0 2.5

133
OMNITAB 6/9/69 FIT EXAMPLE FROM SNEDECOR-COCHRAN PP 135-140 17

LEAST SQUARES FIT OF RESPONSE, COLUMN 1,

AS A POLYNOMIAL OF DEGREE 1. INDEPENDENT VARIABLE IS COLUMN 3 USING 5 NON-ZERO WEIGHTS = 1.0000000

VARIANCE -COVARIANCE MATRIX OF TIE ESTIMATED COEFFICIENTS

TERM 0 1

0 33.969998

1 -.57933331 .010533333

ANALYSIS OF VARIANCE

-DEPENDENT ON ORDER INDEPENDENT VARIABLES ENTERED,

UNLESS VECTORS ARE ORTHOGONAL

-

TERM SS=RED. DUE TO COEF. F(COEF=0) P(F)

0 99404.996 9437.184 .000

1 1904.3999 180.797 .001

RESIDUAL 31.599998 D.F. = 3 TOTAL 101341.00 D.F. = 5

TERM CUM. RESIDUAL MS D.F. F(COEFS=0) P(F)

0 483.99998 4 4808.990 .000

1 10.533333 3 180.797 .001

134
OMNITAB 6/9/69 FIT EXAMPLE FROM SNEDECOR-COCHRAN PP 135-140 18 -9C

LEAST SQUARES FIT OF RESPONSE, COLUMN 1,

AS A POLYNOMIAL OF DEGREE 1. INDEPENDENT VARIABLE IS COLUMN 3 USING 5 NON-ZERO WEIGHTS = 1.0000000

ESTIMATES FROM LEAST SQUARES FIT

TERM COEFFICIENT S.D. OF COEFF. RATIO

0 65.100002 5.8283787

1 1.3800000 .10263203

11.17

13.45

RESIDUAL STANDARD DEVIATION = BASED ON DEGREES OF FREEDOM

3.2455097

5- 2 = 3

ACCURACY*

8.00

8.00

* THE NUMBER OF CORRECTLY COMPUTED DIGITS IN EACH COEFFICIENT

USUALLY DIFFERS BY LESS THAN 1 FROM THE NUMBER GIVEN HERE.

FIT OMITTING LAST TERM

TERM COEFFICIENT S.D. OF COEFF. RATIO

0 141.00000 9.8586990 14.33

22.000000

5- 1 = 4 RESIDUAL STANDARD DEVIATION = BASED ON DEGREES OF FREEDOM

135
OMNITAB 6/9/69 FIT EXAMPLE FROM SNEDECOR-COCHRAN PP 135-140 19

3.75 +-- STANDARDIZED RESIDUALS VS ROW NUMBER

+ + x + + +

2.25 + +

.75+

* * +

.*

-.75+ +

* *_

2.25+ +

3.75 + —

1.0 3.0000 5.0

3.75 +-- STANDARDIZED RESIDUALS VS PREDICTED RESPONSE

2.25+ +

.75+

*

* +

-.75+ +

*

rt _

2.25 + +

3.75 +-- + + x + + + 1.1340+02 1.4100+02 1.6860+02

136
OMNITAB 6/9/69 FIT EXAMPLE FROM SNEDECOR- COCHRAN PP 135-140 20

-90c

LEAST SQUARES FIT OF RESPONSE, COLUMN 1,

AS A POLYNOMIAL OF DEGREE 1. INDEPENDENT VARIABLE IS COLUMN 3 USING 5 NON- ZERO WEIGHTS = 1.0000000

ANALYSIS OF VARIANCE

-DEPENDENT ON ORDER INDEPENDENT VARIABLES ENTERED,

UNLESS VECTORS ARE ORTHOGONAL

TERM SS=RED. DUE TO COEF. F(COEF=0) P(F)

0 99404.996 9437.184 .000

1 1904.3999 180.797 .001

RESIDUAL 31.599998 D.F. = 3 TOTAL 101341.00 D.F. = 5

137
OMNITAB 6/9/69 FIT EXAMPLE FROM SNEDECOR- COCHRAN PP 135-140 21

LEAST SQUARES FIT OF RESPONSE, COLUMN 1,

AS A POLYNOMIAL OF DEGREE 1. INDEPENDENT VARIABLE IS COLUMN 3 USING 5 NON-ZERO WEIGHTS = 1.0000000

ESTIMATES FROM LEAST SQUARES FIT

TERM COEFFICIENT S.D. OF COEFF. RATIO ACCURACY*

0 65.100002 5.8283787 11.17 8.00

1 1.3800000 .10263203 13.45 8.00

RESIDUAL STANDARD DEVIATION = 3.2455097

BASED ON DEGREES OF FREEDOM 5- 2 = 3

* THE NUMBER OF CORRECTLY COMPUTED DIGITS IN EACH COEFFICIENT

USUALLY DIFFERS BY LESS THAN 1 FROM THE NUMBER GIVEN HERE.

138
4 . 5 .1 Selection Of Variables In Linear Regression.

Often an experimenter starts with (k) predictors (independent variables) in a linear

regression problem, but wants to use a model with only (p) predictors, where (p) is often

much smaller than (k) . The objective is to obtain a parsimonious model which describes the

data satisfactorily and is subject to little or no bias.

The problem is often referred to as one of selecting the "best" subset of the (k)

predictor variables. It is not a straightforward problem. The meaning of "best" may. vary

from one problem to another and may not be clear in any one problem. Hocking (1976) gives a comprehensive review of the literature.

Stepwise regression is often used in variable subset selection. The method has a number

of disadvantages. It yields a single answer, which may not be the "best." Both forward

selection and backward selection procedures can be used. They do not necessarily give the

same answer and when they do, the answer is not necessarily the "best" answer. The method

normally yields a single answer when several might be preferable or needed.

Recently, a number of approaches have been taken based on computing regressions for all

possible subsets. Some of these techniques depend heavily on the use of the statistic:

C(p) = RSS (p) + 2p - n,

a where n = number of measurements,

p

= number of variables in the subset,

RSS(p) = residual sum of squares using p variables,

a

2 = an independent estimate of the error variance.

2 Usually, an independent estimate of a is jiot available, and the residual variance from the

full model with (k) variables is used for a .

If the model is satisfactory, C(p) will be approximately equal to p, subject to sampling

variability.

This section describes the BESTCP instruction which computes the ten smallest C(p)

statistics among all possible C(p) statistics with k variables and a constant term in the

full model.

BESTCP of y in col (C) weights (E) with (k) variables in columns (C) . .

. (C)

The structure of a BESTCP instruction is similar to that of a FIT instruction without

storage. The instruction uses a very efficient algorithm to compute only as many

regressions as are needed in order to produce the ten smallest C(p) statistics. The results

may be thought of as being printed in four sections.

First, a two-line title gives the column number (or column heading) for the independent

variable (1st argument) , the number of predictor variables (3rd argument) and the number of

measurements with non-zero weights. The (k) variables are numbered consecutively from 1 to

k and immediately below the title, a table is given relating the variables to the column

numbers (or column headings)

.

139
Second, under titles REGRESSION WITH 1 VARIABLE, REGRESSIONS WITH 2 VARIABLES, etc., the

ten smallest C(p) statistics are printed. There are three columns under each of these C(p)

statistics titled VARIABLE, COEFFICIENT, and F RATIO. The first column gives the specific

variables in the regression and the second column gives the least squares estimates of the

regression coefficients. The F-ratios have 1 and (n-p-1) degrees of freedom. The value of

the F-ratio is influenced by the order of the variables in the regression.

Third, all additional C(p) statistics, obtained as a byproduct of computing the ten

smallest, are printed with titles C(P) STATISTIC and VARIABLES for REGRESSION WITH 1 VARIABLE, REGRESSIONS WITH 2 VARIABLES, etc.

Fourth, at the bottom, the number of regressions needed to find the ten smallest C(p)

statistics is printed along with the number of operations.

Use of Results

C(p) statistics which are approximately equal to p should be looked for. Remember, p

equals the number of variables plus one for the constant term. Among subsets with

comparable C(p) statistics, the subsets with the smallest number of variables are usually

the most desirable. In a particular problem, there may be practical considerations that

should force a variable to be included in the chosen subset. A good analyst will look for

patterns among the low and high C(p) statistics. In the example below it should be noticed

that variable 1 does not appear in any of the subsets with C(p) statistics greater than 7.

The subset with the smallest C(p) statistic should not necessarily be selected. The

small value may be the result of sampling variability. A plot of C(p) versus p can be very

helpful in subset selection.

Example

A set of instructions is listed below to produce C(p) statistics for Hald's data

discussed on pages 89 and 90 of Daniel and Wood (1971) . The results are shown on pages 142

and 143.

WIDTH 80

READ DATA INTO COLUMNS 11 12 13 14 AND 1 7 26 6 60 78,.5 1 29 15 52 74,.3 11 56 8 20 104,.3 11 31 8 47 87,.6 7 52 6 33 95,.9 11 55 9 22 109,.2 3 71 17 6 102,.7 1 31 22 44 72,.5 2 54 18 22 93,.1 21 47 4 26 115,,9 1 40 23 34 83,.8 11 66 9 12 113,.3 10 68 8 12 109,,4 HEAD COL 14/ PCT.

BESTCP 1 1.0 4 11

The coefficients printed below C(P) = 5.496 are the 2nd and 3rd coefficients that would

be obtained by using the instruction

FIT 1, 1.0, 3, 10, 11, 14

with 1.0 in every row of column 10. The F ratios are the same as the F-ratios printed by

FIT in the third column from the right in the analysis of variance given on the third page.

140
Remarks -90c

(a) If (k) is very large, it takes a lot of computing time to compute all possible

regressions. The algorithm used by a BESTCP instruction is believed to be the most

efficient one available. If the number of variables is ten or less, the computing time

should not be very important. However, if the number of variables is large the computing

time could run into minutes depending upon the number of variables, the data and the

computer. The number of operations at the end of the printing may be helpful in assessing

time requirements.

(b) The BESTCP instruction is an exception to the rule in OMNITAB development that

accuracy comes before speed. The results should not be expected to be as accurate as results obtained from an excellent program designed to perform calculations for a single

regression. We believe the accuracy of the results is more than adequate for their purpose

and have no evidence of numerical instability. As a precaution, a FIT instruction could be

used for the subsets under final consideration. This should be done anyway, because further

analysis, including an examination of the residuals, should be done before the final

selection is made.

(c) In the above example, all possible C(p) statistics were printed (excluding the one

for the regression with no variables). This will not always be the case; particularly if k

is large. If m C(p) statistics are printed in addition to the ten smallest, these m

statistics will not necessarily be the m smallest of the remaining C(p) statistics. They

are merely the C(p) statistics which were obtained as a by-product in computing the

regressions necessary to obtain the ten smallest.

(d) It is believed that the BESTCP instruction as it now stands is very powerful and

useful. It represents the best state of the art computing for regression subset selection.

However , with more experience and time it can and should be improved.

Restrictions

(a) The C(p) statistics are only computed for a regression model with a constant term,

but a column number for the constant term must not be in the instruction.

(b) The number of variables (3rd argument of instruction) must be greater than or equal

to 3 and less than or equal to 28. If the number of variables is 3, the instruction only

finds the seven smallest C(p) statistics.

(c) Weights can only be equal tc 0.0 or 1.0. If the second argument is a column

number, all the numbers in the column should be equal to 0.0 or 1.0.

(d) For a given number of variables in a regression (subset size) a maximum of ten C(p)

statistics are printed.

141
OMNITAB 5/19/72 CP STATISTIC STUDY, PAGE 89 OF DANIEL AND WOOD 1

C(P) STATISTICS FOR COLUMN 1 AS A LINEAR FUNCTION OF A CONSTANT AND

FROM ONE UP TO 4 VARIABLES WITH 13 MEASUREMENTS WITH NONZERO WEIGHTS

VARIABLE 1 IS COLUMN 11 , VARIABLE 2 IS COLUMN 12 , VARIABLE 3 IS COLUMN 13 , VARIABLE 4 IS PCT. C2S

REGRESSION WITH 1 VARIABLE

C(P) STATISTIC VARIABLES

138.731 4 142.486 2 202.549 1 315.154 3

REGRESSIONS WITH 2 VARIABLES

C(P) = 2.678

VARIABLE

1

2

COEFFICIENT

1.4683058

.66225047

F RATIO

146.523

208.582

VARIABLE

14

C(P) = 5.496

COEFFICIENT

1.4399583

-.61395362

F RATIO

108.224

159.295

VARIABLE

3

4

C(P) = 22.373

COEFFICIENT

-1.1998512

-.72460013

F RATIO

40.295

100.357

VARIABLE

23

C(P) = 62.438

COEFFICIENT

.73132958

-1.0083862

F RATIO

36.683

11.816

VARIABLE

24

C(P) = 138.226

COEFFICIENT

.31090473

-.45694190

F RATIO

.172

.431

C(P) STATISTIC VARIABLES

198.094 1 3

REGRESSIONS WITH 3 VARIABLES

VARIABLE

1

24

C(P) = 3.018

COEFFICIENT

1.4519380

.41610947

-.23654049

F RATIO

154.008

5.026

1.863

VARIABLE

1

23

C(P) = 3.041

COEFFICIENT

1.6958901

.65691487

.25001752

F RATIO

68.716

220.547

1.832

142
-90c

VARIABLE

C(P) = 3.497

COEFFICIENT F RATIO

1 1.0518541 22.113

3 -.41004334 4.236

4 -.64279614 208.240

VARIABLE

C(P) = 7.337

COEFFICIENT F RATIO

2 -.92341541 12.427

3 -1.4479710 96.940

4 -1.5570444 41.654

REGRESSIONS WITH 4 VARIABLES

VARIABLE

C(P) = 5.000

COEFFICIENT F RATIO

1. 1.5510969 4.337

2 .51016177 .497

3 .10190346 .018

4 -.14406672 .041

14 REGRESSIONS 56 OPERATIONS

143
4.6+ Correlat ion

.

We failed to indicate the maximum size for CORRELATION in the ' 'OMNITAB II User's

Reference Manual." For the number of variables, p, the maximum number of measurements, n

(NRMAX) , allowed is given in the table below. Note, that the maximum value of p is 51 and

that the worksheet would have to be redimensioned to accommodate the maximum number of

measurements. The table assumes the standard worksheet size (and scratch area, 13,500).

R n R n R

n R

n R n

11 1106 21 589 31 388 41 279

2 3372 12 1019 22 561 32 375 42 270

3 3347 13 945 23 536 33 362 43 262

4 2676 14 880 24 512 34 349 44 254

5 2229 15 823 25 491 35 338 45 247

6 1909 16 773 26 471 36 327 46 240

7 1668 17 728 27 452 37 316 47 233

8 1481 18 688 28 435 38 306 48 226

9 1331 19 651 29 418 39 296 49 219

10 1209 20 619 30 403 40 287 50 213

51 207

If the number of measurements exceeds the number in the above table for a particular

value of p, the following fatal error message is printed:

INSUFFICIENT SCRATCH AREA.

4.7.1 Probability Plotting .

CAUCHY PLOT,

EXTREME PLOT,

LAMBDA PLOT,

NORMAL PLOT,

UNIFORM PLOT',

DEXPONENTIAL PLOT,

GAMMA PLOT,

LOGISTIC PLOT,

PARETO PLOT,

WEI BULL PLOT

EXPONENTIAL PLOT,

HALFNORMAL PLOT,

LOGNORMAL PLOT,

POISSON PLOT,

Background . A probability plot is a graphical tool for assessing the goodness of fit of some hypothesized statistical distribution (e.g., normal, Poisson, uniform, etc.) to some observed data. It is a plot of the ordered observations, X^ vertically, versus the

theoretical "typical values", Mj_, for the ordered observations of the hypothesized

distribution, horizontally. The X± values plotted along the vertical axis are only a function of the observed data. The Mj_ values plotted along the horizontal axis are only a function of the number of observations, n, and the hypothesized distribution. The

discriminatory power of a probability plot stems from the fact that the horizontal axis

values, Mi, are strongly affected by the hypothesized distribution. If the user's choice of

a distribution is correct, the Xi and Mi will be (approximately) linearly related and the

resulting probability plot will be close to a straight line. However, if the user's choice

of a distribution is not in accord with the true underlying distribution of the data, then

the Xi and Mi will not have a simple linear relation and the resulting probability plot will

be non-linear.

Advantages . A probability plot is a simple, yet powerful technique for assessing the form of the

underlying distribution of an observed set of data. An analysis of a probability plot will

help answer the following questions.

(1) Are the data normal (or exponential, or ...)?

144
-90g

(2) Are the data longer-tailed or shorter -tailed than normal?

(3) Are certain observations outliers?

(4) Is the experiment under "statistical control?"

(5) Is the assumed regression model correct (an incorrect model will frequently result

in gross non-normality of the residuals)?

(6) Are the t and F tests commonly employed in regression and ANOVA valid (they are

fully valid only if the underlying distribution of residuals is normal)?

(7) What are the optimal (minimum variance) estimators for location and dispersion (the

optimal estimators are dependent on the actual underlying distribution)?

Comparison with other Techniques .

There are several advantages of using a probability plot as opposed to other techniques

for checking distributional assumptions

.

(1) It is easy to use and interpret.

(2) It is a graphical technique and thus it makes use of and presents information about

every element in the data set (outliers, for example, stand out). In this respect,

probability plots are superior to methods which reduce all of the data to a single

statistic (like chi -squared)

.

(3) It needs no a priori estimate of the unknown location and dispersion parameters of

the distribution (as does, e.g., chi-squared).

(4) It uses individual data points and therefore avoids class width and number-ofclasses problems intrinsic to, e.g., histogram methods.

(5) It provides "free" estimates of location and dispersion parameters via the

intercept and slope of the probability plot of the fitted line to the probability

plot.

(6) It is generally applicable to a wide gamut of distributions, distributional

families, and distributional types (continuous and discrete).

(7) When the hypothesized distribution is inappropiate , the plot provides "feedback"

information for a better hypothesized distribution.

Interpreting Probability Plots .

The dominant feature to look for in a probability plot is linearity. If the plot

approximates a straight line (necessarily from the lower left comer to the upper right

comer), then the hypothesized distribution yields a good fit to the observed data. On the

other hand, the more non-linear the probability plot, the worse the distributional fit.

As alluded to previously, under probability plot advantages, an important property of a probability plot is the existence of feedback information. If the choice of a distribution

results in a non-linear plot, then the type of non-linearity can be used to determine a better- fitting choice of a distribution. To be specific, suppose the choice was a particular symmetric distribution with "moderate" tails (like, say, the normal distribution)

and the resulting probability plot was non-linear. If the shape of the plot is vertical,

then horizontal, then vertical (VHV) , then this suggests that the distribution is not longtailed enough, and a distribution which is longer -tailed will probably give a better fit.

Conversely, if we had a horizontal, then vertical, then horizontal (HVH) type probability

plot, then this suggests that a distribution which is shorter-tailed will fit the data

145
better. Finally, if a probability plot has a non -symmetric cross-over (inflection) point

(or no cross-over (inflection) point at all) , then this suggests that a non-symmetric

(skewed) distribution would be an improved choice for fitting the data.

If the hypothesized distribution adequately describes the data, then the probability

plot may show considerable variation about the straight line when the number of measurements

is small. Such variation will, of course, tend to decrease as the number of measurements

increases

.

Estimates .

The probability plot correlation coefficient is a measure of the linearity of the

probability plot and hence the adequacy of the distributional fit. See FILLIBEN (1975) for

percentage points of the PPCC for the normal case.

The intercept and slope of the probability plot serve as estimators of the location

parameter y and the scale parameter a of the distribution, respectively. It is to be noted

that in general y and a do not always correspond to the population mean and standard

deviation of the distribution; however, in the important normal case, such equivalence does

exist.

A statistician should be consulted for help in interpreting the probability plot

correlation coefficient, intercept or slope.

Use of Probability Plots .

It is appropriate to apply probability plots not only to "raw" data, but also to

residuals from a regression or an analysis of variance. In using QMNITAB probability plots,

the user need not order the observations (or residuals) in the column of interest. Such

ordering is done automatically and internally. After a probability plot has been formed,

the column being operated on remains unchanged. The column is exactly as it was before the

probability plot instruction was executed.

The complete form of all the probability plot instructions is given below.

CAUCHY PLOT of column (C)

DEXPONENTIAL PLOT of column (C)

EXPONENTIAL PLOT of column (C)

EXTREME PLOT of column (C)

/

/ EXTREME PLOT with parameter (K) of column (C)

/

“7

/

/

146
GAMMA PLOT with parameter (K) of column (C)

HALFNORMAL PLOT of column (C)

LOGISTIC PLOT of column (C)

LOGNORMAL PLOT of column (C)

LAMBDA PLOT with parameter (K) of column (C)

NORMAL PLOT of column (C)

PARETO PLOT with parameter (K) of column (C)

POISSON PLOT with parameter (K) of column (C)

UNIFORM PLOT of column (C)

WEIBULL PLOT with parameter (K) of column (C)

The nine instructions with first word CAUCHY, DEXPONENTIAL , EXPONENTIAL, EXTREME,

HALFNORMAL, LOGISTIC, LOGNORMAL, NORMAL and UNIFORM have a single argument; the column

number where the data are stored. The five instructions with first word GAMMA, LAMBDA,

PARETO, POISSON and WEIBULL have an additional argument, which appears before the column

number, to specify the value of the parameter of the distribution. The optional form of

EXTREME PLOT also has two arguments for specifying the parameter value and the column

number. The main form of EXTREME PLOT gives an extreme value type 1 probability plot. The

optional form of EXTREME PLOT, with a parameter specified, gives an extreme value type 2 probability plot. The parameter of a LAMBDA PLOT instruction can be negative, zero, or

147
positive. The parameter for the other instructions must always be positive. If the

parameter value is zero or negative, the following fatal error message is printed:

THE PROBABILITY DISTRIBUTION PARAMETER IS NOT ALLOWED.

Three different sizes of probability plots are possible. The vertical axis always has

the same length and the usual number of 51 plotting positions. (We expect to provide a new

instruction for varying this size in the near future.) The size of the horizontal axis is

determined by the use or non-use of a WIDTH instruction. (See Section Cl. 1.1.) The three

possibilities are:

Case (n) of WIDTH (n) Number of Plotting Positions

1 120 51 x 101

2 72-119 51 x 61

3 59-71 51 x 41

If a WIDTH instruction has not been used, n = 120 and the plot is the normal size, i.e.,

51 x 101. If (n) of WIDTH (n) is less than 59, a plot is not given and the following

informative diagnostic is printed:

THE INSTRUCTION WAS IGNORED BECAUSE . .

.

WIDTH = (n) IS TOO SMALL OR TOO LARGE.

The one line title at the top of the plot and the one line of information at the bottom

of the plot are somewhat different for each of the three different sized plots. For a Weibull probability plot with parameter 2.0 and 250 measurements in column 45, the three

possible headings are:

WEIBULL PROBABILITY PLOT OF 250 MEASUREMENTS IN COLUMN 45 WITH PARAMETER = 2.0000000

WEIBULL PR. PLOT OF COLUMN 45 N = 250 PARAMETER 2.0000000

WEIBULL PR PLOT OF COLUMN 45 PARAM. 2.0000000

The line of information at the bottom of the plot, in each case, is:

PROB. PLOT CORR. COEFF. = .9952, ESTIMATES * INTERCEPT = -1.95270000, SLOPE 2.2013169

PLOT COR COEF = .9952, EST* INT. = 1.9527000, SLOPE = 2.2013169

PROBABILITY PLOT CORRELATION COEFF. = .9952

Note, if the WIDTH is less than 72, the number of measurements is not given in the heading

and estimates of the intercept and slope are not printed at the bottom.

Caution.

In addition to the two-word commands EXPONENTIAL PLOT and EXTREME PLOT, we have had for

some time the one -word commands EXPONENTIAL and EXTREMA. When these one -word commands are used PLOT should not be a noise word immediately following the command or the instruction

will be misinterpreted as a probability plot instruction. (PLOT can be a noise word in the

instruction just as long as it does not appear immediately after the command without an intervening argument or noise word.)

148
Example .

The following set of instructions is used to produce the normal probability plot of 200

random normal deviates which appears on the next page. A uniform probability plot on page

151 and a Cauchy plot on page 152 are also given for comparison. Since the data are random

normal deviates, the normal probability plot is approximately a straight line. The uniform

distribution has shorter tails, and hence the probability plot does not resemble a straight

line and has a vertical -horizontal -vertical appearance. The tails of a Cauchy distribution

are longer than the tails of a normal distribution and hence the probability plot has a horizontal -vertical -horizontal appearance

.

OMNITAB 10/24/75 EXAMPLE OF NORMAL PROBABILITY PLOT

LIST OF COMMANDS, DATA AND DIAGNOSTICS

SET TEST DATA IN COLUMN 1 -1.276 -1.,218 -0..453 -0..350 0 ..723 0 ,.676 -1..099 -0..314 -0..394 -0..633

-0.318 -0.,799 -1..664 1 ,.391 0 ..382 0 ,733 0,.653 0 ..219 -0..681 1..129

-1.377 -1.,257 0,.495 -0,.139 -0..854 0 ,.428 -1,.322 -0.,315 -0,.732 -1,.348

2.334 -0.,337 -1,,955 -0..636 -1.,318 -0,.433 0 ..545 0 ..428 -0..297 0,.276

-1.136 0.,642 3,.436 -1..667 0..847 -1,.173 -0..355 0 ,.035 0 ..359 0,.930

0.414 -0.,011 0,,666 -1,,132 -0..410 -1,.077 0 ..734 1..484 -0,.340 0 ,.789

-0.494 0.,364 -1,.237 -0,.044 -0,.111 -0,.210 0 ,.931 0 ,.616 - 0 ,,377 - 0 ,.433

1.048 0 .,037 0 ..759 0 ,.609 -2,,043 - 0 ,.290 0 ,.404 -0..543 0 ..486 0 ,.869

0.347 2.,816 - 0 ..464 - 0 ,.632 -1..614 0 ,.372 - 0 ..074 - 0 ..916 1 ,.314 -0 .038

0.637 0 ,.563 - 0 ,.107 0,.131 -1,.808 -1 .126 0 ,.379 0 ,.610 -0 .364 -2 .626

2.176 0 .,393 - 0 ,.924 1 ,.911 -1..040 -1 .168 0 ,.485 0 ..076 - 0 ,.769 1 .607

-1.185 - 0 ,,944 - 1 ,.604 0 .185 - 0 ,.258 -0 .300 - 0 ,.591 - 0 ,.545 0 ..018 -0 .485

0.972 1 ..710 2,.682 2,.813 - 1 ,.531 -0 .490 2,.071 1,.444 -1 .092 0 ,.478

1.210 0 .,294 -0 .248 0 ,.719 1 ,.103 1 .090 0 ,.212 -1 .185 - 0 ,.338 -1 .134

2.647 0 ..777 0 ,.450 2,.247 1 ,.151 -1 .676 0 ,.384 1 .133 1 ,.393 0 .814

0.398 0..318 -0,.928 2,.416 - 0 ,.936 1 .036 0 ,.024 - 0 ..560 0 ,.203 - 0 ,.871

0.846 -0,.699 -0 .368 0 .344 - 0 ,.926 -0 .797 -1 .404 -1 .472 - 0 ,.118 1 .456

0.654 -0..955 2,.907 1 .688 0 ,.752 -0 .434 0 .746 0 ,.149 -0 .170 -0 .479

0.522 0..231 -0 .619 - 0 ,.265 0 ,419 0 .558 -0 .549 0 ,.192 - 0 ,.334 1 .373

-1.288 -0,.539 - 0 ,.824 0 .244 - 1 ,.070 0 .010 0 ,.482 - 0 ,.469 -0 .090 1 .171

NORMAL PLOT OF RANDOM NORMAL DEVIATES IN COLUMN 1 UNIFORM PLOT OF COLUMN 1 CAUCHY PLOT OF COLUMN 1 STOP

149
CMNITAB 4/12/76 EXAMPLE OF NORMAL PROBABILITY PLOT PAGE 1

NORMAL PROBABILITY PLOT OF 200 MEASUREMENTS IN COLUMN 1 + 3.4360+ *+

2.2236+

*

* *

*

*

*

* ** +

*

1.0112+

X

-.20120+

-1.4136+

*

*

* ** **

** ***

* + **  

** *

***

***

** *** X

***

** +  

** **

**

** ***

***

**  

** ***

*** +

*

* ****

*

*

*

-2.6260+*

4- -2.7011

+ + + + + x + + + + + -1.6206 -.54022 .54022 1.6206 2.7011

PROB. PLOT CORR. COEFF. = .9898, ESTIMATES * INTERCEPT = .043910000, SLOPE = 1.0532417

150
1

kNITAB 4/12/76 EXAMPLE OF NORMAL PROBABILITY PLOT PAGE 2

UNIFORM PROBABILITY PLOT OF 200 MEASUREMENTS IN COLUMN 1 + + + + + + +- - 3.4360+

-+*+

2.2236+

*_ ** _ *

* **

*

***

* **

**

1 . 0112 + ** ***

X

-. 20120 +

*****

***** ********

 

** **

  ** ***

*** *********

*******

 

** * *****

*** *****

  -1.4136+ ** 

* ***

*

-2.6260+*

+ + + + + — -+ + + + .0034597 .20208 .40069 .59931 .79792

PROB. PLOT CORR. COF.FF. = .9641, ESTIMATES * INTERCEPT = -1.7164464, SLOPE = 3.5207127

- -+ .99654

151
CMNITAB 4/12/76 EXAMPLE OF NORMAL PROBABILITY PLOT PAGE 3

CAUCHY PROBABILITY PLOT OF

+ + + 3.4360+

200 MEASUREMENTS IN COLUMN 1

*+

2.2236+

*

* *

*

*

*

** +

*

1.0112+

***

*

****

*

* +

*

***

**

*

* X * X

* .20120+ * +

*

** *

*a

a aa

* 1.4136+ * +

* aaaa

a

h

*

2 .6260+*

+ + + -92.000 -55.200

PROB. PLOT CORR. COEFF. =

+

-18.400 18.400 55.200 92.000

.6276, ESTIMATES * INTERCEPT' = .043910000, SLOPE = .061224883

152
4.8+ References.

DANIEL, CUTHBERT (1959). Use of half-normal plots in interpreting factorial two-level

experiments. Technometrics, 1, No. 4, 311-341.

DANIEL, CUTHBERT and WOOD, FRED S. (1971). Fitting Equations to Data . Wiley

Interscience.

FILLIBEN, JAMES J. (1975). The probability plot correlation coefficient test for

normality. Technometrics , 17, No. 1, 111-117.

FURNIVAL, GEORGE M. and WILSON, ROBERT W. , Jr. (1974). Regressions by leaps and bounds.

Technometrics, 16, 499-511.

GARDNER, D.A. and HULL, N.C. (1966). An approximation to two-sided tolerance limits for

normal populations. Technometrics, 8, 115-122.

GORMAN, J.W. and TOMAN, R.J. (1966). Selection of variables for fitting equations to

data. Technometrics, 8, 27-51.

HAHN, G.J. and SHAPIRO, S.S. (1967). Statistical Methods in Engineering . John Wiley 8 Sons, Inc. Pages 260-308.

HOCKING, R.R. (1976). The analysis and selection of variables in linear regression.

Biometrics, (Invited paper) , 32, 1-49.

JENKINS, G.M. and WATTS, D.G. (1968). Spectral Analysis and its Applications . HoldenDay.

McGILL, ROBERT, TUKEY, JOHN W. and LARSON, W.A. (1977). Variations of box plots.

Submitted to American Statistician.

NELSON, WAYNE and THOMPSON, V.C. (1971). Weibull probability papers. Journal of

Quality Technology, 3, No. 2, 45-50.

NEMENYI, PETER; DIXON, SYLVIA K. and WHITE, NATHANIEL B. , JR. (1975). Statistics

from Scratch . To be published by Holden-Day.

SNEDECOR, G.W. (1956). Statistical Methods , 5th Edition, Iowa State College Press.

TUKEY, J.W. (1970). Exploratory Data Analysis . 3 vols. Preliminary edition. AddisonWesley.

TUKEY, J.W. (1972). Statistical Papers in Honor of George W. Snedecor. Edited by T.A. Bancroft, pp 295-296.

WILK, M.B. and GNANADESIKAN, R. (1968). Probability plotting methods for the analysis

of data. Biometrika, 55, 1-17.

153
5. NUMERICAL ANALYSIS.

5.1* Special Integrals .

COSINTEGRAL, EEXPINTEGRAL, EINTEGRAL, EXPINTEGRAL,

HCOSINTEGRAL, HSININTEGRAL , NEGEINTEGRAL, SININTEGRAL

The special integrals described below are frequently encountered in physical problems.

COSINTEGRAL of (E) put in column (C)

Computes values of the cosine integral

Ci(x) = y

+ ln(x) + J* [ (cos (t) -l)/t]dt , x > 0,

where y

= Euler's constant= 0.57721566...

For the numbers 1.0, 2.0, 3.0, and 20.0 in column 1, the instruction

COSINTEGRAL of column 1 put in column 2

would put the numbers .33740392, .42298083, .11962979, and .044419821 into column 2.

If the value (s) specified by (E) is (are) negative or zero, the following arithmetic

fault message is printed:

FUNCTION NOT DEFINED FOR NEGATIVE OR ZERO VALUES.

However, negative values, which produce complex answers, are allowed in the optional form

with three arguments described below.

/ /

/ COSINTEGRAL of (E) put real values in col (C) and imaginary values in col (C) /

/ /

Computes values of the cosine integral when x is negative. The result is complex with

real and imaginary parts given by

Ci(-x) = Ci(x) - rri

For the numbers -1.0, -2.0, -5.0 and -20.0 in column 3, the instruction

COSINTEGRAL of col 3 put real part in col 7 and imaginary part in col 8

would put the numbers .33740392, .42298083, -.19002975, and .044419821 into column 7 and the

numbers -3.1415927, -3.1415927, -3.1415927 and -3.1415927 into column 8.

If any value specified by (E) is zero, the following arithmetic fault message is

printed

:

FUNCTION NOT DEFINED FOR ZERO VALUES.

154
: EEXPINTEGRAL for (n) of (E) put in column (C) :

Computes values of

exE

n

(x) , x > 0 , n = 0 , 1 , 2 ,

. .

.

,

where E^x) is described below under EXPINTEGRAL; except n must be greater than 1 when x

equals zero.

For the numbers 1.0, 2.0, 3.0, and 20.0 in column 1, the instruction

EEXPINTEGRAL for n = 2 of column 1 put in column 2

would put the numbers .40365263, .27734276, .21374878, and .045629090 into column 2.

If the value (s) specified by (E) is (are) negative, the following arithmetic fault

message is printed:

FUNCTION NOT DEFINED FOR NEGATIVE VALUES.

If x = 0 and n = 0 or 1, the following arithmetic fault message is printed:

FUNCTION NOT DEFINED FOR ZERO VALUES.

If (n) is a negative integer, the following fatal error message is printed:

INCORRECT ARGUMENT IN INSTRUCTION.

If (n) is not an integer, the following fatal error message is given:

IMPROPER TYPE OF ARGUMENT.

: EINTEGRAL of (E) put in column (C)

Computes values of the integral

Ei (x) = - f“x

[e -t/t]dt , x > 0.

For the numbers 1.0, 2.0, 3.0, and 20.0 in column 1, the instruction

EINTEGRAL of column 1 put in column 2

would put the numbers 1.8951178, 4.9542344, 9.9338325, 25,615,653. into column 2.

If the value (s) specified by (E) is (are) negative or zero, the following arithmetic

fault message is printed:

FUNCTION NOT DEFINED FOR NEGATIVE OR ZERO VALUES

.

155
: EXPINTEGRAL for (n) of (E) put in column (C) :

Computes values of the exponential integral

E (x) = /? t n

e

xtdt ; n = 0 , 1 , 2 ,

. .

. , x > 0

;

except n must be greater than 1 when x equals zero.

For the numbers 1.0, 2.0, 3.0 and 20.0 in column 1, the instruction

EXPINTEGRAL for n = 2 of column 1 put in column 2

would put the numbers .14849551, .037534262, .010641925, and 9.4048564-11 into column 2.

If the value (s) specified by (E) is (are) negative, the following arithmetic fault

message is printed:

FUNCTION NOT DEFINED FOR NEGATIVE VALUES.

If x = 0 and n = 0 or 1, the following arithmetic fault is printed:

FUNCTION NOT DEFINED FOR ZERO VALUES.

If (n) is a negative integer, the following fatal error message is printed:

INCORRECT ARGUMENT IN INSTRUCTION.

If (n) is not an integer, the following fatal error message is printed:

IMPROPER TYPE OF ARGUMENT.

: HCOSINTEGRAL of (E) put in column (C) :

Computes values of the hyperbolic cosine integral

Chi(x) = y

+ ln(x) + /* [ (cosh(t) -l)/t]dt , x > 0,

where y

= Euler's constant = 0.57721566...

For the numbers 1.0, 2.0, 3.0, and 20.0 in column 1, the instruction

HCOSINTEGRAL of column 1 put in column 2

would put the numbers .83786694, 2.4526669, 4.9603921, and 12,807,826. into column 2.

If the value (s) specified by (E) is (are) negative or zero, the following arithmetic

fault message is printed:

FUNCTION NOT DEFINED FOR NEGATIVE OR ZERO VALUES.

However, negative values, which produce complex answers, are allowed in the optional form

with three arguments described below.

156
/ /

/ HCOSINTEGRAL o£ (E) put real part in col (C) and imaginary part in col (C) /

/ /

Computes values of the hyperbolic cosine integral when x is negative. The result is

complex with real and imaginary parts given by

Chi(-x) = Chi(x) - mi.

For the numbers -1.0, -2.0, -5.0 and -20.0 in column 3, the instruction

HCOSINTEGRAL of col 3 put real part in col 7 and imaginary part in col 8

would put the numbers .83786694, 2.4526669, 20.092063, and 12,807,826. into column 7 and

the numbers -3.1415927, -3.1415927, -3.1415927 and -3.1415927 into column 8.

If any x-value is zero, the following arithmetic fault message is printed:

FUNCTION NOT DEFINED FOR ZERO VALUES.

HSININTEGRAL of (E) put in column (C)

Computes values of the hyperbolic cosine integral

Shi(x) = / x [sinh(t)/t]dt.

o When x is negative, the integral is evaluated as Shi(-x) = -Shi(x).

For the numbers 1.0, -1.0, 2.0, 3.0, and 20.0 in column 1, the instruction

HSININTEGRAL of column 1 put in column 2

would put the numbers 1.0572509, -1.0572509, 2.5015674, 4.9734405, and 12,807,826. into

column 2.

NEGEINTEGRAL of (E) put in column (C)

Computes values of the integral

e xEi (x) , x > 0

,

where Ei(x) is described above under EINTEGRAL.

For the numbers 1.0, 2.0, 3.0, and 20.0 in column 1, the instruction

NEGEINTEGRAL of column 1 put in column 2

would put the numbers .69717488, .67048271, .49457640, and .052797795 into column 2.

If the value (s) specified by (E) is (are) not positive, the following arithmetic fault

message is printed:

FUNCTION NOT DEFINED FOR NEGATIVE OR ZERO VALUES.
: S ININTEGRAL of (E) put in column (C) :

Computes values of the sine integral

Si(x) = /* [sin(t)/t]dt

.

When x is negative, the integral is evaluated as Si(-x) = -Si(x).

For the numbers 1.0, -1.0, 2.0, 3.0, and 20.0 in column 1, the instruction

SININTEGRAL of column 1 put in column 2

would put the numbers .94608307, -.94608307, 1.6054130, 1.8486525, and 1.5482417 into column

2 .

References .

STEGUN, IRENE A and ZUCKER, RUTH (1974). Automatic computing methods for special

functions. Part II. The exponential integral E (x) . J. of Res., B, NBS, 74B,

199-216. n

STEGUN, IRENE A and ZUCKER, RUTH (1976). Automatic computing methods for special

functions. Part III. The sine, cosine, exponential integrals and related

functions. J. of Res., B, NBS, 80B, 291-311.

5.1.1 Differences .

DIFFERENCES, SDIFFERENCES,

DIVDIFFERENCES , SDIVDIFFERENCES

Differences and, in particular, divided differences are used extensively in numerical

analysis for studying the functional form of data, interpolation, inverse interpolation,

numerical integration and numerical differentiation.

DIFFERENCES of y in column (C)

The DIFFERENCES instruction automatically prints a table of the 1st, 2nd

? ..., 7th

differences; providing NRMAX is at least eight. If NRMAX is less than eight, the

instruction prints the maximum number of differences; namely (NRMAX- 1). The numbers in the

first column are printed on every other line to produce a more readable table.

Ignoring the OMNITAB line at the top of the page, the instructions

SET Y IN COLUMN 2 0, .20134, .30452, .52110

DIFFERENCES OF COLUMN 2

would produce the following printing:

158
COLUMN 2 Y 1ST 2ND 3RD

0 .

.20134000

.30452000

.52110000

The numbers in the designated column appear tinder the title Y. The appropriate column

heading appears above Y. Any entry in the table on the right of the first column is formed

by subtracting the number just above it on the left from the number just below it on the

left. For example, .11340000 = .21685000 - .10318000.

/ /

/ DIFFERENCES of y in column (C) put in columns (C) , (C) , ... , (C) /

/ /

Similar to the above form; except differences are stored in the worksheet in consecutive

rows starting wr ith row 1. The number of arguments determines the number of differences

which are stored. The first differences are stored in the column designated by the 2nd

argument, the second differences are stored in the column designated by the 3rd argument,

etc. and the kth differences are stored in the column designated by the last argument. The

value of k must be less than NRMAX. The appropriate column headings are printed above the

titles 1ST, 2ND, etc.

.20134000

.10318000

.21658000

-.098159997

.11340000

.21156000

SDIFFERENCES of y in column (C) put in columns (C) , (C) , ... , (C)

Same as the above instruction; except the automatic printing is suppressed. If the

number of arguments is one, the following informative diagnostic is printed:

THE INSTRUCTION 1VAS IGNORED BECAUSE . .

.

COMMAND BEGINS WITH S AND STORAGE MUST BE REQUESTED.

DTVDIFFERENCES for x in col (C) and y in col (C)

The DIVDIFFERENCES instruction automatically prints a table of the 1st, 2nd, ..., 6th

divided differences; providing NRMAX is at least seven. If NRMAX is less than seven, the

instruction prints the maximum number’ of divided differences, namely (NRMAX- 1) . The numbers

in the first two columns for x and y are printed on every other line to produce a more

readable table.

Ignoring the CMNITAB line at the top of the page, the instructions

SET X IN COLUMN 1

0 ,

. 2 ,. 3 , .5 SET Y IN COLUMN 2 0 .20134, .30452, .52110

DIVDIFFERENCES FOR X IN COLUMN 1 AND Y IN COLUMN 2

would produce the following printing:

159
COLUMN 1 COLUMN 2 X Y 1ST 2ND 3RD

0. 0. 1.0067000

.20000000 .20134000

1.0318000

.083666841

.17333259

.30000000 .30452000

1.0829000

.17033313

.50000000 .52110000

The first two columns of the table give the values of x and y in the columns designated

by the first two arguments of the instruction. The jth entry in the column for the ith

order divided differences is computed as:

f. . x J , with f . = y .

.

j o»3 J

For example, with i=3 and j=l,

£

3,1 ^2,2

' f

2,l'

)/(- X

4

" X

l'

) ’

= (.17033313 - .083666841)/ (0.5 - 0.0),

= .17333259

/ /

/ DIVDIFFERENCES for x in col (C) and y in col (C) put in cols (C) , (C) , ... , (C) /

/ . /

Similar to the above form; except divided differences are stored in the worksheet. The

number of arguments determines the number of divided differences which are stored. The

first divided differences are stored in the column designated by the 3rd argument, the

second divided differences are stored in the column designated by the 4th argument, etc. and

the kth divided differences are stored in the column designated by the last argument. The

value of k must be less than (NRMAX-1) . The appropriate column headings are printed above

the titles 1ST, 2ND, etc.

SDIVDIFFERENCES for x in (C) and y in (C) put in cols (C) , (C) , ... , (C)

Same as the above form; except the automatic printing is suppressed. If the number of

arguments is two, the following informative diagnostic is printed:

INSTRUCTION WAS IGNORED BECAUSE . .

.

COMMAND BEGINS WITH S AND STORAGE MUST BE REQUESTED.

References .

HAMMING, R. W. (1962). Numerical Methods For Scientists And Engineers . McGraw-Hill

Book Company, Inc. Pages 3-13 and 98-101; in particular.

HILDEBRAND, F. B. (1956). Introduction To Numerical Analysis . McGraw-Hill Book

Company, Inc. Chapter 2; in particular.

160
7. ARRAY OPERATIONS.

7.4* Properties Of An Array .

Minor changes have been made in the printing of the APROPERTIES instructions,

of 71 characters is printed on any line.

A maximum

161
8. MATRIX OPERATIONS.

8.5* Matrix Analysis.

New algorithms are now being used for MiNVERT , MEIGEN and SOLVE which provide

substantially improved accuracy. The MINVERT and SOLVE instructions use routines given in:

FORSYTHE, G.E. and IDLER, C.B. (1967) Computer Solution of Linear Algebraic Systems . Prentice -Hall

.

The MEIGEN instruction uses routines given in:

SMITH, B.T., BOYLE, J.M. , GARBOW, B.S., IKEBE, Y., KLEMA, V.C. and MOLER, C.B. (1974).

Matrix Eigensystem Routines - EISPACK Guide . Lecture Notes in Computer Science, 6,

Springer-Verlag.

The value r=77 on page 212 of NBS TN 552 for the largest matrix which can be inverted

has been increased to 81 (81 is the solution of r2 + 4r = 13,500). (To obtain square

matrices larger than 62x62 but less than 82x82 , the worksheet would have to be

redimensioned .

)

The restriction on the size of matrices which can be used with MEIGEN given on page 211

of NBS TN 552 has been removed. MEIGEN can now be used with any symmetric matrix which will

fit in the worksheet.

Note carefully , a MEIGEN instruction which stores the eigenvalues in the worksheet, now

stores the eigenvalues in ascending order. However, if any eigenvalue is not found, the

eigenvalues are not stored in ascending order and the following informative diagnostic

message is printed

ITERATION FAILED TO FIND AN EIGENVECTOR, (n) UNORDERED VALUES FOUND.

The entries in the matrix shown at the bottom of page 211 of NBS TN 552 are incorrect

and should read:

Row/ Column 3 4 5 6

2 72.0 56.0 -64.0 -64.0

3 56.0 72.0 -64.0 -64.0

4 -64.0 -64.0 72.0 56.0

5 -64.0 -64.0 56.0 72.0

The results of using the instruction

162
MEIGEN matrix A in row 2, col 3 size 4x4, put values in col 7, vectors in 2,8

gives the following results instead of those shown at the top of page 212 of NBS TN 552.

Eigenvalues Eigenvectors

Row/Column 7 Row/Column 8^ 9 10 11

1 -1.1802143-07 2 .50000000 -2.3403118-08 .70710678 .50000000

1 15.999998 T .49999999 -1.5923433-08 -.70710678 .50000000

3 16.000000 4 .49999995 -.70710680 4.4801595-09 -.49999998

£

2.5599998+02 F .50000003 .70710674 0. -.50000001

8.6* Matrix Properties.

Numerous minor changes have been made in the printing produced by a MPROPERTIES

instruction. The following two pages show the results of using the set of instructions on

page 218 of NBS TN 552 and in effect replace pages 219 and 220 of NBS TN 552. If a MLABEL

instruction has been used, the label is used in the title of the printing.

163
QMNITAB 4/27/71 EXAMPLE OF MPROPERTIES

PROPERTIES OF THE 4X4 MATRIX STARTING AT (1,1)

ROW - FOR PROPERTIES STORED IN COLUMN 10

GENERAL

1 TRACE ( 4 VALUES USED) 101.00000

2 TRACE NUMBER TWO 1255.0000

3 MAXIMUM NUMBER 36.000000

4 MINIMUM NUMBER 4.0000000

5 MAXIMUM NUMBER IN ABSOLUTE VALUE 36.000000

6 MINIMUM NUMBER IN ABSOLUTE VALUE 4.0000000

7 MINIMUM NON- ZERO ABSOLUTE VALUE 4.0000000

8 NUMBER OF POSITIVE NUMBERS 16

9 NUMBER OF ZERO NUMBERS 0 10 NUMBER OF NEGATIVE NUMBERS 0

11 SUM OF TERMS 303.00000

12 AVERAGE 18.937500

13 SUM OF SQUARES 7691.0000

14 SUM OF SQUARES ABOUT MEAN 1952.9375

15 WITHIN ROWS SUM OF SQUARES 1112.7500

16 WITHIN COLS SUM OF SQUARES 1112.7500

17 SUM OF ABSOLUTE VALUES 303.00000

18 AVERAGE OF ABSOLUTE VALUES 18.937500

SPECIFIC

19 DETERMINANT 1023.9998

20 RANK 4 NORMS

21 SQUARE ROOT OF SUM OF B(I,J)**2 .0000027

22 N*MAXIMUM(B(I , J)

)

.000012

23 MAXIMUM VALUE OF ROW SUM .0000025

24 NORMALITY YES* (1)

25 SYMMETRY YES* (1)

26 SKEW SYMMETRY NO*(0)

27 DIAGONAL ITY NO* (0)

28 ORTHOGONALI TY : A'A = I NO* (0)

29 A'A = DIAGONAL MATRIX NO* (0)

30 TRIANGULAR NO**(0)

31 STOCHASTIC (ROW AND/OR COL SUMS=1) NO*** (0)

* IF ANSWER IS NO: (R,C)= 0. IF ANSWER IS YES: (R,C)= 1, IF EXACT OR

(R, ,C)= 2, IF TOLERANCE IS MET.

TRIANGULAR

** (R,C)=0 , IF ANSWER IS NO. (R,C)=1, IF UPPER PART OF MATRIX=0.

(R,C)=2 , IF LOWER PART=0. (R,C)=3, IF ALL OFF DIAGONAL TERMS=0.

STOCHASTIC

*** (R,C)=0 , IF MATRIX IS NOT STOCHASTIC. (R,C)=1, IF EACH ROW SUM=1.

(R,C)=2 , IF EACH COL SUM=1. (R,C)=3, IF EACH ROW AND COL SUM=1.

PG 1

164
OMNITAB 4/27/71 EXAMPLE OF MPROPERTIES PG 2

PROPERTIES OF THE 3X4 MATRIX STARTING AT (1,1)

GENERAL

1 TRACE ( 3 VALUES USED) 65.000000

2 TRACE NUMBER TWO 644.00000

3 MAXIMUM NUMBER 36.000000

4 MINIMUM NUMBER 4.0000000

5 MAXIMUM NUMBER IN ABSOLUTE VALUE 36.000000

6 MINIMUM NUMBER IN ABSOLUTE VALUE 4.0000000

7 MINIMUM NON- ZERO ABSOLUTE VALUE 4.0000000

8 NUMBER OF POSITIVE NUMBERS 12

9 NUMBER OF ZERO NUMBERS 0 10 NUMBER OF NEGATIVE NUMBERS 0

11 SUM OF TERMS 200.00000

12 AVERAGE 16.666667

13 SUM OF SQUARES 4666.0000

14 SUM OF SQUARES ABOUT MEAN 1332.6667

15 WITHIN ROWS SUM OF SQUARES 740.00000

16 WITHIN COLS SUM OF SQUARES 829.33334

17 SUM OF ABSOLUTE VALUES 200.00000

18 AVERAGE OF ABSOLUTE VALUES 16.666667

SPECIFIC

28 ORTHOGONALITY: A'A = I NO*(0)

29 A'A = DIAGONAL MATRIX NO* (0)

* (R,C)=0, IF MATRIX IS NOT ORTHOGONAL

.

(R,C)=1 OR 2, IF MATRIX IS ORTHOGONAL ROW WISE.

(R,C)=3 OR 4, IF MATRIX IS ORTHOGONAL COLUMN WISE

.

( (R,C)=I : IF 1=1 OR 3, ORTHOGONALITY IS EXACT;

IF 1=2 OR 4, RELATIVE WITHIN ERROR BOUND OF .IE -6.)

165
11. + INDEX OF COMMANDS DECSRIBED IN PART C SUPPLEMENT

An asterisk immediately following the page number indicates that the corresponding

instruction has been modified in some way. Otherwise, the command is new.

ALABEL 23 FIXED 26* PROPORTIONS 50

APROPERTIES 161* FOURPLOTS 31 RANGE 51

AVERAGE 49* FRANCAIS 26 READ UNIT "L" 47

BACKSPACE UNIT "L" 46 FULL 17 READ UNIT "L" "L" 47

BESTCP 139 GAMMA PLOT 147 RECODE 58

BRIEF 17 HALFNORMAL PLOT 147 RE^TE 19

CALCOMP AXIS 39 HISTOGRAM 63* REPLACE 58

CALCOMP FAST 40 HCOSINTEGRAL 156 RETAIN 59

CALCOMP PAPER 39 HSININTEGRAL 157 REWIND UNIT ”L" 47

CALCOMP PLOT 36 INTERACTIVE 17 SDIFFERENCES 159

CALCOMP SIZE 39 ITALIANO 26 SDIVDIFFERENCES 160

CALCOMP SLOW 40 JAPANESE 26 SET UNIT "L" 47

CALCOMP SPEED 40 LABEL 23 SKIP UNIT "L" 47

CALCOMP TAPE "LM 41 LAMBDA PLOT 147 SININTEGRAL 158

CAUCHY PLOT 146 LENGTH 18 SLOVENE 26

CENSOR EQ 52 LOCAL 19 SOLVE 162*

CENSOR GE 52 LOGISTIC PLOT 147 SSTEM LEAF 71

CENSOR GT 52 LOGNORMAL PLOT 147 STATISTICAL 96*

CENSOR LE 52 MEDIAN 50 STATPLOTS 72 CENSOR LT 52 MEIGEN 162* STDDEV 51

CENSOR NE 52 MINVERT 162* STEM LEAF 64

CHOOSE 54 MLABEL 24 SVENSKA 26

CODE 55 MPROPERTIES 163* TABLE AVERAGE 80

CORRELATION 144* NCPLOT 31 TABLE FREQUENCY 80

COSINTEGRAL 154 NEDERLANDS 26 TABLE MAXIMUM 81

CPLOT 26 NEGEINTEGRAL 157 TABLE MEDIAN 81

CREAD UNIT "L" 46 NORMAL PLOT 147 TABLE MINIMUM 81

CREAD UNIT "L" "L" 46 NORSK 26 TABLE PERCENTAGE 81

CSET UNIT "L" 46 NPLOT 32 TABLE PROPORTION 81

DANSK 26 NTABLE AVERAGE 79 TABLE RANGE 81

DAYS 49 NTABLE FREQUENCY 79 TABLE STDDEV 81

DELETE 56 NTABLE MAXIMUM 79 TABLE SUM 81

DEUTSCH 26 NTABLE MEDIAN 79 TERMINAL 20

DEXPONENTIAL PLOT 146 NTABLE MINIMUM 79 TWOPLOTS 32

DIFFERENCES 158 NTABLE PERCENTAGE 79 TWOWAY 116*

DIVDIFFERENCES 159 NTABLE PROPORTION 79 UNIFORM PLOT 147

EEXP INTEGRAL 155 NTABLE RANGE 79 UNIT 48

EINTEGRAL 155 NTABLE STDDEV 80 VOCABULARY 26

ENDFILE UNIT "L" 47 NTABLE SUM 80 WEI BULL PLOT 147

ENGLISH 26 OMIT 57 WIDTH 20

ESPANOL 26 ONEWAY 104* WRITE UNIT "L" 48

EXPINTEGRAL 156 PARETO PLOT 147 WRITE UNIT "L" "L" 48

EXPONENTIAL PLOT 146 PERCENTAGES 50 YUGOSLAV 26

EXTREME PLOT 146 POISSON PLOT 147

FIT 125* PORTUGESE 26

166
PART D SUPPLEMENT

LIST OF INSTRUCTIONS

167
LIST OF INSTRUCTIONS

***** *****

ALABEL (label,) (R,) (C , ) (r,) (c,), (label,) (R,) (C,) (r,) (c,), ... V,C

AVERAGE of numbers in cols (C) , (C) ,

. .

. , (C) put in cols (C) , (C) ,

. .

. , (C) E

***** g *****

BACKSPACE UNIT "L" (r) logical records 1 BESTCP of y in col (C) weights (E) with (k) variables in columns (C) . .

. (C) V,P

BRIEF $ no arguments 0

***** £ *****

CALCOMP AXIS (K) height of y-axis, (K) width of x-axis 2 CALCOMP FAST or zip mode 0 CALCOMP PAPER (n) height of paper 1 CALCOMP PLOT (n) curves (E) options columns (C) , (C) , . .

. , (C) versus (C) V

CALCOMP PLOT (n) , (E) vertical scales (K) to (K) , (C) , (C) , . . . , (C) vs (C) V

CALCOMP PLOT (n) , (E) , (C) , . .

. , (C) vs (C) horizontal scales from (K) to (K) V

CALCOMP PLOT (n) , (E) , ordinate (K) , (K) , (C) vs (C) abscissa (K) , (K) V

CALCOMP PLOT (n) curves, (E) options (C) vs (C) , (C) vs (C) , . . . , (C) vs (C) V

CALCOMP PLOT (n) , (E) , vertical scale (K) , (K) , (C) vs (C) , . . . , (C) vs (C) V

CALCOMP PLOT (n) , (E) , (C) vs (C) , . .

. , (C) vs (C) horizontal scale (K) , (K) V

CALCOMP PLOT (n) , (E) vert. (K) , (K) , (C) vs (C) , . .

. , (C) vs (C) horiz. (K) , (K) V

CALCOMP SIZE (n) height of paper 1 CALCOMP SIZE (K) height of vertical or y-axis 1 CALCOMP SIZE (n) height of paper (K) height of vertical axis 2 CALCOMP SIZE (K) height of vertical axis, (K) length of horizontal axis 2 CALCOMP SIZE (n) height of paper, (K) vertical height, (K) horizontal width 3 CALCOMP SLOW mode 0 CALCOMP SPEED (n) $ n=0 for slow mode, n=l for fast mode 1 CALCOMP TAPE ”LM unit 0 CAUCHY PLOT of column (C) 1,P

CENSOR EQ col (C) for values equal to (E) , replace by (E) , put in col (C) 4

(C)=COLUMN number; (E)=EITHER col number or constant; (K)=CONSTANT; (N)=instr. number

N=affect NRMAX; P=print; S=synonym; V=args variable; W=work below NRMAX; X=no storage

A=abbreviat ion ; B=pelow NRMAX; C=cannot store; D=args odd; E=args even; M=must store;

(Rj=ROW number; (small letter) =arways integer; qualifier ,!L"= LETTER A,B,C,D,E or F.

168
CENSOR GE col (C) nos greater than or eq. to (E) , replace by (E) , put in (C) 4 CENSOR GT col (C) for values greater than (E) , replace by (E) , put in col (C) 4 CENSOR LE col (C) nos less than or equal to (E) , replace by (E) , put in (C) 4 CENSOR LT col (C) for values less than (E) , replace by (E) , put in col (C) 4 CENSOR NE col (C) for values not equal to (E) , replace by (E) , put in col (C) 4 CHOOSE rows with (K) in column (C) and put in column (C) 3,N

CHOOSE rows with numbers between (K) and (K) in column (C) and put in col (C) 4,N

CHOOSE rows with (K) in (C) , corresp. rows of (C) , . . . , (C) put in (C) , . .

. , (C) E,N,V

CHOOSE from (K) to (K) in (C) , corr. rows of (C) , . .

. , (C) put in (C) , . . . , (C) D,V,N

CODE column (C) using length (K) put in column (C) 3 CODE column (C) starting at (K) using length (K) put in column (C) 4 COS INTEGRAL of (E) put in column (C) 2 COSINTEGRAL of (E) put real values in col (C) and imaginary values in col (C) 3 CPLOT col (C) , symbol (E) col (C) , . . . symbol (E) versus column (C) D,P

CPLOT col (C) sym (E) , (C) , . .

. (E) vertical scale from (K) to (K) vs col (C) D,P

CPLOT col (C) sym (E) , (C) , . .

. (E) vs col (C) horiz. scale from (K) to (K) D,P

CPLOT (C) (E) , (C) , . .

. (E) vertical (K) to (K) vs (C) horizontal (K) to (K) D,P

CREAD UNIT "L", using (r) logical records, into columns (C) , (C) , ... (C) V,S,N

CREAD UNIT "L" "L" format, using (n) blocks, into cols (C) ... (C) V,S,N

CSET UNIT "L", using (r) logical records, into column (C) 2,S,N

CSET UNIT "L", using (r) logical records, into row (R) of column (C) 3,S,N

***** p

*****

DANSK 0 DAYS for month (E) day (E) and year (E) put in column (C) 4 DELETE rows having (K) in rows of any cols (C) , . . . , (C) put in cols (C) , . .

. , (C) V,D,N

DELETE rows with values between (K) and (K) in (C) , . .

. , (C) put in (C) , . .. , (C) E,V,N

DEUTSCH 0 DEXPONENTIAL PLOT of column (C) 1,P

DIFFERENCES of y in column (C) 1,P,X

DIFFERENCES of y in column (C) put in columns (C) , (C) , ... , (C) V,P

DIVDIFFERENCES for x in col (C) and y in col (C) 2,P,X

DI\T)IFFERENCES for x in col (C) and y in col (Q put in (C) , (C) , ... , (C) V,P

***** p

*****

EEXPINTEGRAL for (n) of (E) put in column (C) 3 EINTEGRAL of (E) put in column (C) 2 ENDFILE UNIT "L" 0,S

ENGLISH 0 ESPANOL 0 EXPINTEGRAL for (n) of (E) put in column (C) 3 EXPONENTIAL PLOT of column (C) 1,P

EXTREME PLOT of column (C) 1,P

EXTREME PLOT with parameter (K) of column (C) 2,P

(C)=COLUMN number; (E)=EITHER col number or constant; (X) =CONSTANT ; (N)=instr. number

N=affect NRMAX ; P=print; S=synonym; V=args variable; W=work below NRfvlAX ; X=no storage

A=abbreviation; B=pelow NRMAX; C=cannot store; D=args odd; E=args even; M=must store;

(Rj=ROW number; (small letter) =always integer; qualifier "L"= LETTER A,B,C,D,E or F.

169
***** p *****

FOURPLOTS of (C) vs (C) and (C) vs CC) and (C) vs (C) and (C) vs (C) 8,P

FOURPLOTS (C) , (K) , (K) , (C) , (K) , (K) , (C) , (K) (K) , (C) , (K) (K) (C) , (K) (K) , (C) , (K) (K) E,P

FRANCAIS 0

FULL $ no arguments 0

***** g *****

GAMMA PLOT with parameter (K) of column (C) 2,P

***** pi *****

HALFNORMAL PLOT of column (C) 1 ,P

HCOSINTEGRAL of (E) put in column (C) 2 HCOSINTEGRAL of (E) put real part in col (C) and imaginary part in col (C) 3

HSININTEGRAL of (E) put in column (C) 2

***** j *****

INTERACTIVE use from a terminal 0,C

INTERACTIVE use with (c) characters per line 1,C

ITALIANO 0

***** j

*****

JAPANESE 0

***** p *****

LABEL (label,) (label,) (label,) ... V,C

LABEL (label,) (C,), (label,) (C,), (label,) (C,) ... V,C

LAMBDA PLOT with parameter (K) of column (C) 2,P

LENGTH equal to (n) lines printed per page 1 LOCAL printing at terminal 0

LOGISTIC PLOT of column (C) 1,P

LOGNORMAL PLOT of column (C) 1,P

***** jq *****

MEDIAN of numbers in column (C) put in column (C) 2

(C)=COLUMN number; (E)=EITHER col number or constant; (K)=CONSTANT; (N)=instr. number

N=affect NRMAX; P=print; S=synonym; V=args variable; W=work below NRMAX; X=no storage

A=abbreviation; B=below NRMAX; C=cannot store; D=args odd; E=args even; M=must store;

(R7=R0W number; (small letter) =always integer; qualifier "L"= LETTER A,B,C,D,E or F.

170
MEDIAN of numbers in cols (C) , (C) ,

. .

. , (C) put in cols (C) , (C) ,

. .

. , (C) E MLABEL (label,) (R,) (C,) (r,) (c,), (label,) (R,) (C,) (r,) (c,), ... V,C

***** *****

NCPLOT (C) , (E) , . .

. , (C) , (E) versus column (C)

NCPLOT (C) , (C) , . .

. , (C) , (E) vertical scale (K) to (K) vs col (C)

NCPLOT (C) , (E) , . .

. , (C) , (E) vs col (C) horizontal scale (K) to (K)

NCPLOT (C) , (C) , . .

. , (C) , (E) vertical (K) to (K) vs (C) horizontal (K) to (K)

NEDERLANDS

NEGEINTEGRAL of (E) put in column (C)

NORMAL PLOT of column (C)

NORSK

NPLOT columns (C) , (C) ,

. .

.

,

(C) versus column (C)

NPLOT cols (C) , (C) ,

. .

.

,

(C) vertical scale from (K) to (K) versus col (C)

NPLOT cols (C) ... (C) versus column (C) with horiz . scale from (K) to (K)

NTABLE AVERAGE (n) way with levels in cols (C) , (C) ,. .

.

,

(C) for data in (C)

NTABLE AVERAGE (n) way, levels (C) ... (C) , data in (C) start storing in (C)

NTABLE FREQUENCY (n) way with levels in cols (C) , (C) ,

. .

.

,

(C)

NTABLE FREQUENCY (n) way, levels in (C) , (C) , ..., (C) start storing in (C)

NTABLE MAXIMUM (n) way, levels in cols (C) , (C) , ..., (C) for data in col (C)

NTABLE MAXIMUM (n) way, levels (C) ... (C) data in (C) start storing in (C)

NTABLE MEDIAN (n) way with levels in cols (C) , (C) ,

. .

.

,

(C) for data in col (C)

NTABLE MEDIAN (n) way, levels (C) , ..., (C) data in (C) start storing in (C)

NTABLE MINIMUM (n) way with levels in cols (C) , (C) ... (C) for data in col (C)

NTABLE MINIMUM (n) way, levels (C) ... (C) data in (C) start storing in (C)

NTABLE PERCENTAGE (n) way with levels in cols (C) , (C) ,. .

.

,

(C)

NTABLE PERCENTAGE (n) way, levels in (C) , (C) , ..., (C) start storing in (C)

NTABLE PROPORTION (n) way with levels in cols (C) , (C) ,. . .

,

(C)

NTABLE PROPORTION (n) way, levels in (C) , (C) , . .

.

,

(C) start storing in (C)

NTABLE RANGE (n) way with levels in cols (C) , (C) ,

. .

.

,

(C) for data in col (C)

NTABLE RANGE (n) way, levels (C) , ..., (C) data in (C) start storing in (C)

NTABLE STDEV (n) way with levels in cols (C) , (C) ,. .

.

,

(C) for data in col (C)

NTABLE STDDEV (n) way, levels (C) ... (C) data in (C) start storing in (C)

NTABLE SUM (n) way with levels in cols (C) , (C) ,

. .

.

,

(C) for data in col (C)

NTABLE SUM (n) way, levels (C) , ..., (C) data in (C) start storing in (C)

D,P

D,P

D,P

D,P

0

21,P

0V,P

V,P

V,P

V,P,X

V,P

V,P,X

V,P

v,p,x

V,P

V,X,P

V,P

V,P,X

V,P

v,p,x

V,P

V,P,X

v,p

V,P,X

V,P

V,P,X

V,P

V,P,X

V,P

***** Q

*****

CMIT rows with (K) in column (C) and put in column (C) 3,N

OMIT rows with numbers between (K) and (K) in column (C) and put in col (C) 4 ,N

CMIT rows with (K) in col (C) , corr. rows of (C) , . .

. , (C) put in (C) , . .. , (C) E,V,N

CMIT from (K) to (K) in (C) , corresp. rows of (C) , . .

. , (C) put in (C) , . .

.

,

(C) D,V,N

***** p *****

PARETO PLOT with parameter (K) of column (C) 2,P

PERCENTAGES of numbers on column (C) put in column (C) 2

(C)=COLUMN number; (E)=EITHER col number or constant; (K)=CONSTANT; (N)=instr. number

N=affect NRMAX; P=prin.t; S=synonym ; V=args variable; W=work below NRMAX; X=no storage

A=abbreviation; B=below NRMAX; C=cannot store; D=args odd; E=args even; M=must store;

(RJ=ROW number; (small letter) =always integer; qualifier "L"= LETTER A,B,C,D,E or F.

171
PERCENTAGES of numbers in cols (C) ... (C) put in cols (C) ... (C) E

POISSON PLOT with parameter (K) of column (C) 2,P

PORTUGESE 0 PROPORTIONS of numbers in column (C) put in column (C) 2 PROPORTION of numbers in cols (C) ... (C) put in cols (C) ... (C) E

***** *****

RANGE of numbers in column (C) put in column (C) 2 RANGE of numbers in cols (C) ... (C) put in cols (C) ... (C) E

READ UNIT ”L" into columns (C) , (C) ... (C) V,N,S

READ UNIT "L" "L" format into columns (C) , (C) ,

. .

. (C) V,N,S

RECODE column (C) into column (C) 2 REMOTE printing on high-speed printer 0 REPLACE the number (K) in column (C) by the number (K) and put in column (C) 4 REPLACE the numbers from (K) to (K) in col (C) by (K) and put in column (C) 5 RETAIN numbers between (K) and (K) in cols (C),...,(C) put in cols (C),...,(C) E,V,N

REWIND UNIT "L" 0,S

***** g *****

SDIFFERENCES of y in column (C) put in columns (C) , (C) , ... , (C) V

SDIVDIFFERENCES for x in (C) and y in (C) put in cols (C) , (C) , ... , (C) V

SET UNIT "L" into column (C) 1,N,S

SET UNIT "L" starting with row (R) of column (C) 2,N,S

SKIP UNIT "L", forward (r) logical records 1,S

SININTEGRAL of (E) put in column (C) 2 SLOVENE 0 SSTEM LEAF of column (C) put scrawl in column (C) 2 SSTEM LEAF of col (C) put scrawl in col (C) and depth in col (C) 3,B

SSTEM LEAF col (C) for (i) , (j), (k) and (&), put scrawl in col (C) 6

SSTEM LEAF of (C) for (i) , (j), (k) and (£) , put scrawl in (C) depth in (C) 7,B

STATPLOTS of column (C) 1,P

STDDEV of numbers in column (C) put in column (C) 2 STDEV of numbers in cols (C) ... (C) put in cols (C) ... (C) E

STEM LEAF of column (C) 1,P,X

STEM LEAF of column (C) and put scrawl in column (C) 2,P

STEM LEAF of col (C) , scrawl in (C) and put depth in column (C) 3,P,B

STEM LEAF of column (C) for parameter values (k) , (j), (k) and (£) 5,P,X

STEM LEAF of col (C) for (k) , (j), (k) and (£) put scrawl in col (C) 6,P

STEM LEAF of col (C) for (i) , (j), (k) (£,) put scrawl in (C) and depth in (C) 7,P,B

SVENSKA 0

***** -p *****

TABLE AVERAGE (n) way with levels in cols (C) , (C) , ..., (C) for data in col (C) V,P,X

TABLE AVERAGE (n) way, levels (C) , ..., (C) data in (C) start storing in (C) V,P

TABLE FREQUENCY (n) way with levels in cols (C) , (C) , ..., (C) V,P,X

(C)=COLUMN number; (E)=EITHER col number or constant; (K)=CONSTANT; (N)=instr. number

N=affect NRMAX; P=print; S=synonym; V=args variable; W=work below NRMAX; X=no storage

A=abbreviation; B=below NRMAX; C=cannot store; D=args odd; E=args even; M=must store;

(RJ=R0W number; (small letter) =always integer; quali£Ter "L"= LETTER A,B,C,D,E or F.

172
TABLE FREQUENCY (n) way, levels in (C) , (C) , (C) start storing in (C) V,P

TABLE MAXIMUM (n) way with levels in cols (C) , (C) , (C) for data in col (C) V,P,X

TABLE MAXIMUM (n) way, levels (C) ,

. .

.

,

(C) data in (C) start storing in (C) V,P

TABLE MEDIAN (n) way with levels in cols (C) , (C) , ..., (C) for data in col (C) V,X,P

TABLE MEDIAN (n) way, levels (C) ,

. .

.

,

(C) data in (C) start storing in (C) V,P

TABLE MINIMUM (n) way with levels in cols (C) , (C) , ..., (C) for data in col (C) V,P,X

TABLE MINIMUM (n) way, levels (C) ,

. .

.

,

(C) data in (C) start storing in (C) V,P

TABLE PERCENTAGE (n) way with levels in cols (C) , (C) , ..., (C) V,P,X

TABLE PERCENTAGE (n) way, levels in (C) , (C) ,. .

.

,

(C) start storing in (C) V,P

TABLE PROPORTION (n) way with levels in cols (C) , (C) ,

. .

.

,

(C) V,P,X

TABLE PROPORTION (n) way, levels in (C) , (C) , ..., (C) start storing in (C) V,P

TABLE RANGE (n) way with levels in cols (C) , (C) , ..., (C) for data in col (C) V,P,X

TABLE RANGE (n) way, levels (C) ,. . .

,

(C) data in (C) start storing in (C) V,P

TABLE STDEV (n) way with levels in cols (C) , (C) , ..., (C) for data in col (C) V,P,X

TABLE STDDEV (n) way, levels (C) ,. .

.

,

(C) data in (C) start storing in (C) V,P

TABLE SUM (n) way with levels in cols (C) , (C) , ..., (C) for data in col (C) V,P,X

TABLE SUM (n) way, levels (C) , ..., (C) data in (C) start storing in (C) V,P

TAPE with (c) characters per logical record 1,C

TAPE with (c) characters/logical record and (r) logical records/block 2,C

TERMINAL $ no arguments 0 TWOPLOTS of column (C) versus column (C) and column (C) versus column (C) 4,P

TWOPLOTS col (C) (K) to (K) vs (C) (K) to (K) and (C) (K) (K) vs (C) (K) (K) E,V,P

***** *****

UNIFORM PLOT of column (C) 1,P

UNIT with (c) characters per logical record 1,C

UNIT with (c) characters/logical record and (r) logical records/block 2,C

***** y

*****

VOCABULARY 0,P

***** ^ *****

WEIBULL PLOT with parameter (K) of column (C) 2,P

WIDTH set to a maximum of (c) characters per line 1 WRITE UNIT "L" from columns (C) , (C) . .

. (C) V,S

WRITE UNIT "L" "L" format, from columns (C) , (C) ... (C) V,S

***** y

*****

YUGOSLAV 0

(C) COLUMN number; (E) CITHER col number or constant; (K) CONSTANT; (N)=instr. number

N=affect MMX; P=print; S=synonym; V=args variable; W=work below NRMAX; X=no storage

A=abbreviation; B=below NRMAX; C=cannot store; D=args odd; E=args even; M=must store;

(Rj=ROW number; (small letter) =always integer; qualifier "L"= LETTER A,B,U,D,E or F.

173
NBS-1 14A (REV. 7-73)

U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET

1. PUBLICATION OR REPORT NO. NBSIR 77-12762. Gov’t Accession

No.

3. Recipient’s Accession No.

4. TITLE AND SUBTITLE

OMNITAB II User's Reference Manual 1977 Supplement

5. Publication Date July 1977

6. Performing Organization Code

NBSIR 77-1276

7. AUTHOR(S)

David Hogben and Sally T. Peavy

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

10. Project/Task/Work Unit No.

11. Contract/Grant No.

12. Sponsoring Organization Name and Complete Address (Street, City, State, ZIP

)

National Highway Traffic Safety Administration

Department of Transportation

Washington, D. C. 20590

13. Type of Report & Period Covered Final

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here.) The Supplement describes all the additions and improvements made to the

OMNITAB II computing system at NBS since 1970. OMNITAB 1977, as OMNITAB II is now

known, is fully interactive. Major new capabilities now exist in the following

areas: Use of labels, table making, plotting, numerical analysis, editing of data,

st em-and-leaf displays, selection of variables in linear regression and probability

plotting. The Supplement is an interim document for use until OMNITAB 1977 is

released and new documentation prepared.

17.

18.

KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name; separated by semicolons) Automatic printing; Calcomp plotting; data editing;

interactive; labels; numerical analysis; OMNITAB II computing system; probability

plotting; selection of variables in linear regression; statistical plotting; stemand-leaf displays; table making.

AVAILABILITY Hg Unlimited

1 | For Official Distribution. Do Not Release to NTIS

Order From Sup. of Doc., U.S. Government Printing Office Washington, D.C. 20402, SD Cat. No. Cl 3 B Order From National Technical Information Service (NTIS)

Springfield, Virginia 22151

19. SECURITY CLASS 21. NO. OF PAGES

(THIS REPORT)

175

UNCL ASSIFIED

20. SECURITY CLASS 22. Price (THIS PAGE)

$ 6.75

UNCLASSIFIED

USCOMM-DC 29042- P 74

U.S. GOVERNMENT PRINTING OFFICE: 1977- 240-848:273