NBS Special Publication 747

Statistical Concepts in Metrology — With a Postscript

on Statistical Graphics

Harry H, Ku

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dCioo

NBS Special Publication 747

Statistical Concepts in Metrology — With a Postscript

on Statistical Graphics

Harry H. Ku

Statistical Engineering Division

Center for Computing and Applied Mathematics

National Engineering Laboratory

National Bureau of Standards

Gaithersburg, MD 20899

li

August 1988

U.S. Department of Commerce

C. Williain Verity, Secretary

National Bureau of Standards

Ernest Ambler, Director

I
Library of Congress U.S. Government Printing Office

Catalog Card Number: 88-600569 Washington: 1988

National Bm^eau of Standards

Special Publication 747

Natl. Bur. Stand. (U.S.),

Spec. Publ. 747,

48 pages (Aug. 1988)

CODEN: XNBSAV

For sale by the Superintendent

of Documents,

U.S. Government Printing Office,

Washington, DC 20402
Contents

Statistical Concepts of a Measurement Process 1 Arithmetic Numbers and Measurement Numbers 1 Computation and Reporting of Results 2

Properties of Measurement Numbers 3

The Limiting Mean 3

Range, Variance, and Standard Deviation 4

Population and the Frequency Curve 4

The Normal Distribution 6

Estimates of Population Characteristics 8

Interpretation and Computation of Confidence Interval and Limits ... 9

Precision and Accuracy 11

Index of Precision 11

Interpretation of Precision 12

Accuracy 13

Statistical Analysis of Measurement Data 13

Algebra for the Manipulation of Limiting Means and Variances 14

Basic Formulas 14

Propagation of Error Formulas 16

Pooling Estimates of Variances 18

Component of Variance Between Groups 19

Comparison of Means and Variances 20

Comparison of a Mean with a Standard Value 20

Comparison Among Two or More Means 21

Comparison of Variances or Ranges 23

Control Charts Technique for Maintaining Stability and Precision 24

Control Chart for Averages 24

Control Chart for Standard Deviations 25

Linear Relationship and Fitting of Constants by Least Squares 28

References 29

Postscript on Statistical Graphics 31

Plots for Summary and Display of Data 31

Stem and Leaf 31

Box Plot 33

Plots for Checking on Models and Assumptions 35

Residuals 36

Adequacy of Model 36

Testing of Underlying Assumptions 38

Stability of a Measurement Sequence 40

Concluding Remarks 42

References 42

iii
List of Figures

2-1 A symmetrical distribution 5

2-2 (A) The uniform distribution. (B) The log-normal distribution 5

2-3 Uniform and normal distribution of individual measurements

having the same mean and standard deviation, and the corresponding distribution(s) of arithmetic means of four independent

measurements 7

2-4 Computed 90% confidence intervals for 100 samples of size 4

drawn at random from a normal population with m = 10, cr = 1 . . . 11

2-5 Control chart on x for NB'IO gram 25

2-6 Control chart on s for the calibration of standard cells 26

1 Stem and leaf plot. 48 values of isotopic ratios, bromine (79/81). . . 32

2 Box plot of isotopic ratio, bromine (79/91) 34

3 Magnesium content of specimens taken 35

4 Plot of deflection vs load 37

5 Plot of residuals after linear fit 37

6 Plot of residuals after quadratic fit 38

7 Plot of residuals after linear fit. Measured depth of weld defects vs true depth 39

8 Normal probability plot of residuals after quadratic fit 39

9 Differences of linewidth measurements from NBS values. Measurements on day 5 inconsistent with others— Lab A 40

10 Trend with increasing linewidths— Lab B 41

11 Significant isolated outliers — Lab C 41

12 Measurements (% reg) on the power standard at 1-year and

3-month intervals 42

List of Tables

2-1 Area under normal curve between m—kcr and m + kcr 6

2-2 A brief table of values of f 10

2-3 Propagation of error formulas for some simple functions 17

2-4 Estimate of a from the range 19

2-5 Computation of confidence limits for observed corrections,

NB'IO gm 21

2-6 Calibration data for six standard cells 27

1 Y—Ratios 79/81 for reference sample 32

iv
statistical Concepts in

IVIetrology-Witli a

Postscript on Statistical

Graphics

Harry H. Ku

statistical Engineering Division, National Bureau of Standards, Gaithersburg, MD 20899

"Statistical Concepts in Metrology" was originally written as Chapter 2

for the Handbook of Industrial Metrology published by the American Society

of Tool and Manufacturing Engineers, 1967. It was reprinted as one of 40

papers in NBS Special Publication 300, Volume I, Precision Measurement and

Calibration; Statistical Concepts and Procedures, 1969. Since then this chapter has been used as basic text in statistics in Bureau-sponsored coxirses and seminars, including those for Electricity, Electronics, and Analytical Chemistry.

While concepts and techniques introduced in the original chapter remain

valid and appropriate, some additions on recent development of graphical

methods for the treatment of data would be useful. Graphical methods can be

used effectively to "explore" information in data sets prior to the application of classical statistical procedures. For this reason additional sections on statisti- cal graphics are added as a postscript

Key words: graphics; measurement; metrology; plots; statistics; uncertainty.

Arithmetic Numbers and Measurement

Numbers

In metrological work, digital numbers are used for different purposes

and consequently these numbers have different interpretations. It is therefore

important to differentiate the two types of numbers which will be encountered.

Arithmetic numbers are exact numbers. 3, e, or tt are all exact numbers by definition, although in expressing some of these numbers in

digital form, approximation may have to be used. Thus, tt may be written

as 3.14 or 3.1416, depending on our judgment of which is the proper one to use from the combined point of view of accuracy and convenience. By the

STATISTICAL CONCEPTS OF

A MEASUREMENT PROCESS

1
usual rules of rounding, the approximations do not differ from the exact

values by more than ±0.5 units of the last recorded digit. The accuracy of

the result can always be extended if necessary.

Measurement numbers, on the other hand, are not approximations to exact numbers, but numbers obtained by operation under approximately

the same conditions. For example, three measurements on the diameter of

a steel shaft with a micrometer may yield the following results:

No. Diameter in cm General notation

1 0.396 .Y, 2 0.392 X,

3 0.401 Xs Sum 1.189 71 2 X,

i = l

1 " Average 0.3963 x =^ — 2 n 1 Range 0.009 R = x,,,,,^ - .

There is no rounding off here. The last digit in the measured value

depends on the instrument used and our ability to read it. If we had used

a coarser instrument, we might have obtained 0.4,0.4, and 0.4; if a finer

instrument, we might have been able to record to the fifth digit after the

decimal point. In all cases, however, the last digit given certainly does

not imply that the measured value differs from the diameter D by less than

±0.5 unit of the last digit.

Thus we see that measurement numbers differ by their very nature from

arithmetic numbers. In fact, the phrase "significant figures" has little meaning

in the manipulation of numbers resulting from measurements. Reflection on

the simple example above will help to convince one of this fact. Computation and Reporting of Results. By experience, the metrologist can usually select an instrument to give him results adequate for his needs,

as illustrated in the example above. Unfortunately, in the process of computation, both arithmetic numbers and measurement numbers are present,

and frequently confusion reigns over the number of digits to be kept in successive arithmetic operatipns.

No general rule can be given for all types of arithmetic operations. If the

instrument is well-chosen, severe rounding would result in loss of information. One suggestion, therefore, is to treat all measurement numbers as exact numbers in the operations and to round off the final result only.

Another recommended procedure is to carry two or three extra figures

throughout the computation, and then to round off the final reported value

to an appropriate number of digits.

The "appropriate" number of digits to be retained in the final result depends on the "uncertainties" attached to this reported value. The term

"uncertainty" will be treated later under "Precision and Accuracy"; our

only concern here is the number of digits in the expression for uncertainty.

A recommended rule is that the uncertainty should be stated to no more

than two significant figures, and the reported value itself should be stated

2
to the last place affected by the qualification given by the uncertainty state- ment. An example is:

"The apparent mass correction for the nominal 10 g weight is +0.0420 mg with an overall uncertainty of 1^ 0.0087 mg using three standard deviations as a limit to the effect of random errors of

measurement, the magnitude of systematic errors from known sources being negligible."

The sentence form is preferred since then the burden is on the reporter

to specify exactly the meaning of the term uncertainty, and to spell out its components. Abbreviated forms such as a ± b, where a is the reported

value and b a measure of uncertainty in some vague sense, should always

be avoided.

Properties of Measurement Numbers

The study of the properties of measurement numbers, or the Theory of

Errors, formally began with Thomas Simpson more than two hundred years

ago, and attained its full development in the hands of Laplace and Gauss.

In the next subsections some of the important properties of measurement

numbers will be discussed and summarized, thus providing a basis for the

statistical treatment and analysis of these numbers in the following major

section.

The Limiting Mean. As shown in the micrometer example above, the

results of repeated measurements of a single physical quantity under essentially

the same conditions yield a set of measurement numbers. Each member of

this set is an estimate of the quantity being measured, and has equal claims on its value. By convention, the numerical values of these n measurements

are denoted by jc,, Xj, . . . , Xn-, the arithmetic mean by x, and the range by

R, i.e., the difference between the largest value and the smallest value

obtained in the n measurements.

If the results of measurements are to make any sense for the purpose at hand, we must require these numbers, though different, to behave as a group in a certain predictable manner. Experience has shown that this is indeed the case under the conditions stated in italics above. In fact, let us adopt as the postulate of measurement a statement due to N. Ernest

Dorsey (reference 2)*

"The mean of a family of measurements—of a number of measure- ments for a given quantity carried out by the same apparatus, procedure, and observer—approaches a definite value as the number of

measurements is indefinitely increased. Otherwise, they could not

properly be called measurements of a given quantity. In the theory

of errors, this limiting mean is frequently called the 'true' value,

although it bears no necessary relation to the true quaesitum, to the

actual value of the quantity that the observer desires to measure.

This has often confused the unwary. Let us call it the limiting mean."

Thus, according to this postulate, there exists a limiting mean m to which X approaches as the number of measurements increases indefinitely,

or, in symbols x — w as « —^ oo. Furthermore, if the true value is t, there

is usually a difference between m and t, or A w — t, where A is defined

as the bias or systematic error of the measurements.

*References'are listed at the end of this chapter.

3
In practice, however, we will run into difficulties. The value of m cannot

be obtained since one cannot make an infinite number of measurements.

Even for a large number of measurements, the conditions will not remain

constant, since changes occur from hour to hour, and from day to day.

The value of t is unknown and usually unknowable, hence also the bias. Nevertheless, this seemingly simple postulate does provide a sound foundation to build on toward a mathematical model, from which estimates can

be made and inference drawn, as will be seen later on. Range, VarianccandStandardDeviation. The range ofn measurements, on the other hand, does not enjoy this desirable property of the arithmetic mean. With one more measurement, the range may increase but cannot

decrease. Since only the largest and the smallest numbers enter into its calculation, obviously the additional information provided by the measure- ments in between is lost. It will be desirable to look for another measure

of the dispersion (spread, or scattering) of our measurements which will

utilize each measurement made with equal weight, and which will approach

a definite number as the number of measurements is indefinitely increased.

A number of such measures can be constructed; the most frequently

used are the variance and the standard deviation. The choice of the variance

as the measure of dispersion is based upon its mathematical convenience

and maneuverability Variance is defined as the value approached by the

average of the sum of squares of the deviations of individual measurements

from the limiting mean as the number of measurements is indefinitely

increased, or in symbols: — 2 (-Yi — my — cr^ = variance, as n —> oo n The positive square root of the variance, a, is called the standard deviation

(of a single measurement); the standard deviation is of the same dimensionality as the limiting mean.

There are other measures of dispersion, such as average deviation and

probable error. The relationships between these measures and the standard

deviation can be found in reference 1. Population and the Frequency Curve. We shall call the limiting mean m

the location parameter and the standard deviation a the scale parameter of

the population of measurement numbers generated by a particular measure- ment process. By population is meant the conceptually infinite number of

measurements that can be generated. The two numbers m and a describe

this population of measurements to a large extent, and specify it completely

in one important special case. Our model of a measurement process consists then of a defined population of measurement numbers with a limiting mean m and a standard

deviation a. The result of a single measurement X* can take randomly any

of the values belonging to this population. The probability that a particular

measurement yields a value of X which is less than or equal to x' is the

proportion of the population that is less than or equal to x', in symbols

P{X < x'} =--- proportion of population less than or equal to x'

Convention is followed in using the capital X to represent the value that might be

produced by employing the measurement process to obtain a measurement (i.e., a random

variable), and the lower case x to represent a particular value of X observed.

4
Similar statements can be made for the probability that X will be greater

than or equal to x" , or for X between x' and x" as follows: P[X > x"}, or P{x' <X< x"].

For a measurement process that yields numbers on a continuous scale,

the distribution of values of X for the population can be represented by

a smooth curve, for example, curve C in Fig. 2-1. C is called a frequency

curve. The area between C and the abscissa bounded by any two values

(.V, and X2) is the proportion of the population that takes values between

the two values, or the probability that X will assume values between x, and .v,. For example, the probability that X < x' , can be represented by

the shaded area to the left of v'; the total area between the frequency curve and the abscissa being one by definition.

Note that the shape of C is not determined by m and cr alone. Any

curve C enclosing an area of unity with the abscissa defines the distribution

of a particular population. Two examples, the uniform distribution and

the log-normal distribution are given in Figs. 2-2A and 2-2B. These and

other distributions are useful in describing certain populations.

Rg. 2-1. A symmetrical distribution.

m

I I 1 L_ I l_J

-2<T -cr +a +20'

0 a 2cr 3 4a 5a 6 a

Rg. 2-2. (A) The uniform distribution (B) The log-normal distribution.

.5
The Normal Distribution. For data generated by a measurement process, the following properties are usually observed:

1. The results spread roughly symmetrically about a central value.

2. Small deviations from this central value are more frequently found

than large deviations.

A measurement process having these two properties would generate a fre- quency curve similar to that shown in Fig. 2-1 which is symmetrical and

bunched together about m. The study of a particular theoretical representation of a frequency curve of this type leads to the celebrated bell-shaped

normal curve (Gauss error curve.). Measurements having such a normal

frequency curve are said to be normally distributed, or distributed in accordance with the normal law of error. The normal curve can be represented exactly by the mathematical

expression

y = _L e-'/2[(x-m)Va'l (•2.0) V 27r cr where y is the ordinate and x the abscissa and e = 2.71828 is the base of

natural logarithms.

Some of the important features of the normal curve are:

1. It is symmetrical about w.

2. The area under the curve is one, as required.

3. If a is used as unit on the abscissa, then the area under the curve between constant multiples of a can be computed from tabulated

values of the normal distribution. In particular, areas under the curve

for some useful intervals between m ^ ka and m ka are given in Table 2-1. Thus about two-thirds of the area lies within one a of m, more than 95 percent within 2a of m, and less than 0.3 percent beyond

3cr from w.

Table 2-1 . Area under normal curve between m -ka- and m+ka

k: 0.6745 1.00 1.96 2.00 2.58 3.00 Percent area under

curve (approx.): 50.0 68.3 95.0 95.5 99.0 99.7

4. From Eq. (2-0), it is evident that the frequency curve is completely

determined by the two parameters m and a. The normal distribution has been studied intensively during the past

century. Consequently, if the measurements follow a normal distribution, we can say a great deal about the measurement process. The question

remains: How do we know that this is so from the limited number of

repeated measurements on hand?

The answer is that we don't! However, in most instances the metrologist

may be willing

1. to assume that the measurement process generates numbers that fol- low a normal distribution approximately, and act as if this were so,

2. to rely on the so-called Central Limit Theorem, one version of which

is the following*: "If a population has a finite variance and mean

w, then the distribution of the sample mean (of n independent

*From Chapter 7, Inrroduction to the Theory of Statistics, by A. M. Mood, McGrawHill Book Company, New York, 1950.

.6
measurements) approaches the normal distribution with variance

crV" and mean m as the sample size n increases." This remarkable

and powerful theorem is indeed tailored for measurement processes.

First, every measurement process must by definition have a finite mean and variance. Second, the sample mean x is the quantity of

interest which, according to the theorem, will be approximately

normally distributed for large sample sizes. Third, the measure of

dispersion, i.e., the standard deviation of the sample mean, is reduced

by a factor of 1/v^ ! This last statement is true in general for all measurement processes in which the measurements are "independent"

and for all n. It is therefore not a consequence of the Central Limit

Theorem. The theorem guarantees, however, that the distribution of

sample means of independent measurements will be approximately

normal with the specified limiting mean and standard deviation

aj-sTn for large n. In fact, for a measurement process with a frequency curve that is symmetrical about the mean, and with small deviations from the mean as compared to the magnitude of the quantity measured, the normal approximation to the distribution of x becomes very good even for n as small as 3 or 4. Figure 2-3 shows the uniform and normal distribution having the same mean and standard deviation. The peaked curve is actually two curves, representing the distribution of arithmetic means of four independent

measurements from the respective distributions. These curves are indis- tinguishable to this scale.

-.6 -.4 -.2 0 .2 4 .6

Fig. 2-3. Uniform and normal distribution of individual measure- ments having the same mean and standard deviation, and

the corresponding distribution(s) of arithmetic means of

fovir independent measurements.

A formal definition of the concept of "independence" is out of the scope

here. Intuitively, we may say that /; normally distributed measurements are independent if these measurements are not correlated or associated m any

7
way. Thus, a sequence of measurements showing a trend or pattern are not

independent measurements.

There are many ways by which dependence or correlation creeps into a set of measurement data; several of the common causes are the following:

1. Measurements are correlated through a factor that has not been

considered, or has been considered to be of no appreciable effect on the results.

2. A standard correction constant has been used for a factor, e.g., temperature, but the constant may overcorrect or undercorrect for

particular samples.

3. Measurements are correlated through time of the day, between days,

weeks, or seasons.

4. Measurements are correlated through rejection of valid data, when

the rejection is based on the size of the number in relation to others

of the group.

The traditional way of plotting the data in the sequence they are taken, or in some rational grouping, is perhaps still the most effective way of

detecting trends or correlation.

Estimates of Population Characteristics. In the above section it is shown

that the limiting mean ni and the variance completely specify a measure- ment process that follows the normal distribution. In practice, m and a'^ are not known and cannot be computed from a finite number of measurements. This leads to the use of the sample mean jc as an estimate of the

limiting mean m and 5', the square of the computed standard deviation of

the sample, as an estimate of the variance. The standard deviation of the

average of n measurements, cr/v^, is sometimes referred to as the standard

error of the mean, and is estimated by sj^sfn . We note that the making of n independent measurements is equivalent

to drawing a sample of size n at random from the population of measurements. Two concepts are of importance here:

1. The measurement process is established and under control, meaning

that the limiting mean and the standard deviation do possess definite

values which will not change over a reasonable period of time.

2. The measurements are randomly drawn from this population, implying

that the values are of equal weights, and there is no prejudice in the

method of selection. Suppose out of three measurements the one

which is far apart from the other two is rejected, then the result will not be a random sample.

For a random sample we can say that x is an unbiased estimate of m,

and 5^ is an unbiased estimate of a% i.e., the limiting mean of x is equal to m and of s'^ to where

1 " X = — D Xi n , = i and

E - xY

n — \ ' n \

In addition, we define

s = = computed standard deviation

Examples of numerical calculations of x and and s are shown in Tables 2-5 and 2-6.

_8
Interpretation and Computation of

Confidence Interval and Limits

By making k sets of n measurements each, we can compute and arrange

k, xs, and s's in a tabular form as follows:

Set

12

Sample mean

X2

Sample standard deviation

S2

K Xk: Sic In the array of ;t's, no two will be likely to have exactly the same value.

From the Central Limit Theorem it can be deduced that the Jc's will be

approximately normally distributed with standard deviation ajVli. The

frequency curve of x will be centered about the limiting mean m and will have the scale factor al^/~n . In other words, x -- m will be centered about

zero, and the quantity

X — m z = j=

cr/V n

has the properties of a single observation from the "standardized" normal

distribution which has a mean of zero and a standard deviation of one. From tabulated values of the standardized normal distribution it is known

that 95 percent of z values will be bounded between —1.96 and +1.96.

Hence the statement

-1.96 <

m < +1.96

or its equivalent,

1.96 < w < X + 1.96

will be correct 95 percent of the time in the long run. The interval

X — 1.96(0-/ V n ) to X + \.96{(7/^y~n ) is called a confidence interval for m. The probability that the confidence interval will cover the limiting mean,

0.95 in this case, is called the confidence level or confidence coefficient. The

values of the end points of a confidence interval are called confidence limits.

It is to be borne in mind that x will fluctuate from set to set, and the interval

calculated for a particular Xj may or may not cover m.

In the above discussion we have selected a two-sided interval symmetrical about X. For such intervals the confidence coefficient is usually

denoted hy \ — a, where a/2 is the percent of the area under the frequency

curve of z that is cut off from each tail. In most cases, a is not known and an estimate of o- is computed from

the same set of measurements we use to calculate x. Nevertheless, let us form a quantity similar to z, which is

^ _ X — m

9
and if we know the distribution of t, we could make the same type of state- ment as before. In fact the distribution of r is known for the case of normally

distributed measurements.

The distribution of / was obtained mathematically by William S. Gosset

under the pen name of "Student," hence the distribution of t is called the

Student's distribution. In the expression for /, both .v and s fluctuate from

set to set of measurements. Intuitively we will expect the value of / to be

larger than that of z for a statement with the same probability of being

correct. This is indeed the case. The values of t are listed in Table 2-2.

Table 2-2. A brief table of values of t

Degrees of Gonndence Level

;

1

1 — na freedom

V 0.500 0.900 0.950 0.990

1 1.000 6.314 12.706 63.657 2 .816 2.920 4.303 9.925

3 .765 2.353 3.182 5.841 4 .741 2.132 2.776 4.604

5 .727 2.015 2.571 4.032 6 .718 1 .943 2.447 3.707 7 .711 1.895 2.365 3.499 10 .700 1.812 2.228 3.169

15 .691 1.753 2.131 2.947 20 .687 1.725 2.086 2.845 30 .683 1.697 2.042 2.750 60 .679 1.671 2.000 2.660

CO .674 1.645 1.960 2.576

*Adapted from Biometrika Tables for Statisticians, Vol. I, edited by E. S. Pearson and H. O. Hartley, The University Press, Cambridge, 1958.

To find a value for /, we need to know the "degrees of freedom" {v)

associated with the computed standard deviation s. Since x is calculated

from the same n numbers and has a fixed value, the nXh value of .Vj is completely determined by x and the other {n — 1).y values. Hence the degrees

of freedom here are n — \. Having the table for the distribution of t, and using the same reasoning

as before, we can make the statement that

X — t ^ < w < X + / f

and our statement will be correct 100 ( 1 — a:) percent of the time in the long

run. The value of t depends on the degrees of freedom p and the probability level. From the table, we get for a confidence level of 0.95, the following lower and upper confidence limits:

V L, = X — t{sl's/~n ) Lu — X + t{s/\/~n )

1 X - \2J06(s/V~n) X + \2J06(sl^)

2 X - 4.302{s/^) X + 4.303{s/^)

3 X - 3.182(5/V^) X + 3.182(jf/^/7r)

The value of / for = cxd is 1.96, the same as for the case of known a. Notice that very little can be said about m with two measurements. However,

for n larger than 2, the interval predicted to contain m narrows down steadily,

due to both the smaller value of t and the divisor a/ ri

.

10
It is probably worthwhile to emphasize again that each particular confidence interval computed as a result of n measurements will either include

m or fail to include m. The probability statement refers to the fact that if we make a long series of sets of n measurements, and if we compute a confidence interval for m from each set by the prescribed method, we would

expect 95 percent of such intervals to include m.

1

1

I I I I T I I I

1

1

1

1 1

1

1 1

1 1 1

1 1 L 1 1 1

1 1 1 1 1 i 1

.

0 10 20 30 40 50 60 70 80 90 100

Fig. 2-4. Computed 90% confidence intervals for 100 samples of size 4 drawn at random from a normal population with m=lQ, o- = 1.

Figure 2-4 shows the 90 percent confidence intervals (P =^ 0.90) computed

from 100 samples of n = 4 from a normal population with m = 10, and a = \. Three interesting features are to be noted:

1. The number of intervals that include m actually turns out to be 90,

the expected number.

2. The surprising variation of the sizes of these intervals.

3. The closeness of the mid-points of these intervals to the line for the

mean does not seem to be related to the spread. In samples No. 2

and No. 3, the four values must have been very close together, but

both of these intervals failed to include the line for the mean. From the widths of computed confidence intervals, one may get an

intuitive feeling whether the number of measurements n is reasonable and

sufficient for the purpose on hand. It is true that, even for small n, the

confidence intervals will cover the limiting mean with the specified probability, yet the limits may be so far apart as to be of no practical significance.

For detecting a specified magnitude of interest, e.g., the difference between

two means, the approximate number of measurements required can be

solved by equating the half-width of the confidence interval to this difference

and solving for n, using a when known, or using by trial and error if a is not known. Tables of sample sizes required for certain prescribed conditions are given in reference 4.

Precision and Accuracy

Index of Precision. Since a is a measure of the spread of the frequency

curve about the limiting mean, a may be defined as an index of precision.

Thus a measurement process with a standard deviation cr, is said to be more precise than another with a standard deviation o-j if cr, is smaller than

cTj. (In fact, a is really a measure of imprecision since the imprecision is directly proportional to a.)

11
Consider the means of sets of n independent measurements as a new

derived measurement process. The standard deviation of the new process

is cr/v^. It is therefore possible to derive from a less precise measurement

process a new process which has a standard deviation equal to that of a more precise process. This is accomplished by making more measurements.

Suppose = Wo, but cr, = la.,. Then for a derived process to have

a[ = a-2, we need

, cr, 2cr.,

or we need to use the average of four measurements as a single measurement.

Thus for a required degree of precision, the number of measurements,

and needed for measurement processes I and II is proportional to the

squares of their respective standard deviations (variances), or in symbols

Hi = ^

Ho

If a is not known, and the best estimate we have of cr is a computed

standard deviation s based on n measurements, then s could be used as an

estimate of the index of precision. The value of s, however, may vary considerably from sample to sample in the case of a small number of measure- ments as was shown in Fig. 2-4, where the lengths of the intervals are constant multiples of s computed from the samples. The number n or the

degrees of freedom u must be considered along with s in indicating how

reliable an estimate s is of a. In what follows, whenever the terms standard

deviation about the limiting mean (a), or standard error of the mean (cr^), are used, the respective estimates s and s/\/^ may be substituted, by taking

into consideration the above reservation. In metrology or calibration work, the precision of the reported value is an integral part of the result. In fact, precision is the main criterion by w hich

the quality of the work is judged. Hence, the laboratory reporting the value

must be prepared to give evidence of the precision claimed. Obviously an

estimate of the standard deviation of the measurement process based only on a small number of measurements cannot be considered as convincing

evidence. By the use of the control chart method for standard deviation

and by the calibration of one's own standard at frequent intervals, as subsequently described, the laboratory may eventually claim that the

standard deviation is in fact known and the measurement process is stable,

with readily available evidence to support these claims.

Interpretation of Precision. Since a measurement process generates

numbers as the results of repeated measurements of a single physical quantity

under essentially the same conditions, the method and procedure in obtaining

these numbers must be specified in detail. However, no amount of detail would cover all the contingencies that may arise, or cover all the factors

that may affect the results of measurement. Thus a single operator in a single day with a single instrument may generate a process with a precisuMi

index measured by cr. Many operators measuring the same quantit\ o\er

a period of time with a number of instruments will yield a precision index

measured by a'. Logically a' must be larger than cr, and in practice it is usually considerably larger. Consequently, modifiers of the words "precision"

are recommended by ASTM* to qualify in an unambiguous manner what

*"Use of the Terms Precision and Accuracy as Applied to the Measurement of a Property of a Material," ASTM Designation, EI77-61T, 1961.

12
is meant. Examples are "single-operator-machine," "multi-laboratory,"

"single-operator-day," etc. The same publication warns against the use of

the terms "repeatability" and "reproducibility" if the interpretation of these

terms is not clear from the context. The standard deviation a or the standard error a/V « can be considered

as a yardstick with which we can gage the difference between two results obtained as measurements of the same physical quantity. If our interest is to compare the results of one operator against another, the single-operator

precision is probably appropriate, and if the two results differ by an amount

considered to be large as measured by the standard errors, we may conclude

that the evidence is predominantly against the two results being truly equal. In comparing the results of two laboratories, the single-operator precision

is obviously an inadequate measure to use, since the precision of each

laboratory must include factors such as multi-operator-day-instruments.

Hence the selection of an index of precision depends strongly on the

purposes for which the results are to be used or might be used. It is common

experience that three measurements made within the hour are closer together

than three measurements made on, say, three separate days. However,

an index of precision based on the former is generally not a justifiable

indicator of the quality of the reported value. For a thorough discussion on the realistic evaluation of precision see Section 4 of reference 2.

Accuracy. The term "accuracy" usually denotes in some sense the close- ness of the measured values to the true value, taking into consideration

both precision and bias. Bias, defined as the difference between the limiting mean and the true value, is a constant, and does not behave in the same way as the index of precision, the standard deviation. In many instances,

the possible sources of biases are known but their magnitudes and directions are not known. The overall bias is of necessity reported in terms of estimated

bounds that reasonably include the combined effect of all the elemental

biases. Since there are no accepted ways to estimate bounds for elemental

biases, or to combine them, these should be reported and discussed in sufficient detail to enable others to use their own judgment on the matter.

It is recommended that an index of accuracy be expressed as a pair of

numbers, one the credible bounds for bias, and the other an index of precision, usually in the form of a multiple of the standard deviation (or

estimated standard deviation). The terms "uncertainty" and "limits of error"

are sometimes used to express the sum of these two components, and their

meanings are ambiguous unless the components are spelled out in detail.

STATISTICAL ANALYSIS

OF MEASUREMENT DATA

7n the last section the basic concepts of a measurement process were

given in an expository manner. These concepts, necessary to the statistical analysis to be presented in this section, are summarized and reviewed below.

By making a measurement we obtain a number intended to express quantitatively a measure of "the property of a thing." Measurement numbers

differ from ordinary arithmetic numbers, and the usual "significant figure"

treatment is not appropriate. Repeated measurement of a single physical

13
quantity under essentially the same conditions generates a sequence of

numbers .v,, .Vo, . . . , .v„. A measurement process is established if this conceptually infinite sequence has a limiting mean m and a standard deviation a. For many measurement processes encountered in metrology, the sequence

of numbers generated follows approximately the normal distribution,

specified completely by the two quantities m and a. Moreover, averages of

/; independent measurement numbers tend to be normally distributed with

the limiting mean m and the standard deviation o-/V n , regardless of the

distribution of the original numbers. Normally distributed measurements

are independent if they are not correlated or associated in any way. A

sequence of measurements showing a trend or pattern are not independent

measurements. Since m and a are usually not known, these quantities are estimated by calculating x and s from n measurements, where

I ^ ^ = — S n 1 and

2 (Xi

The distribution of the quantity t = (x — m)/(5'/V^) (for .v normally

distributed) is known. From the tabulated values of / (see Table 2-2), confidence intervals can be constructed to bracket m for a given confidence

coefficient 1 — a (probability of being correct in the long run).

The confidence limits are the end points of confidence intervals defined by

L,= X ~

Lu = X + t

where the value of / is determined by two parameters, namely, the degrees

of freedom v associated with s and the confidence coefficient 1 — a. The width of a confidence interval gives an intuitive measure of the

uncertainty of the evidence given by the data. Too wide an interval may

merely indicate that more measurements need to be made for the objective

desired.

Algebra for the Manipulation of Limiting

Means and Variances

Basic Formulas. A number of basic formulas are extremely useful in dealing with a quantity which is a combination of other measured quantities.

1. Let nij, and m,j be the respective limiting means of two measured

quantities X and Y, and a, b be constants, then

tn^^.i -= — niy \ (2-1)

Wax+ftv = ci^x + bmy J

2. If, in addition, X and Y are independent, then it is also true that

m^,j = m^my (2-2)

For paired values of X and Y, we can form the quantity Z, with

Z = iX-mJ(Y-my) (2-3)

14
Then by formula (2-2) for independent variables,

= (w^ — — niy) = 0

Thus ni; = 0 when X and Y are independent.

3. The limiting mean of Z in (2-3) is defined as the covariance of X

and y and is usually denoted by cov (A', Y), or a^y. The covariance, similar

to the variance, is estimated by

1 n

- 2 (A-, ~- x){}\ - y) (2-4)

(2-7)

Thus if X and Y are correlated in such a way that paired values are likely to be both higher or lower than their respective means, then s_cy tends to be

positive. If a high x value is hkely to be paired with a low y value, and vice

versa, then s^y tends to be negative. If X and Y are not correlated, s_,y tends

to zero (for large n). 4. The correlation coefficient p is defined as:

p - (2-5)

and is estimated by

f = li± ^ S (-Vt - x){yi - y) 2 6)

Both p and r lie between — 1 and + 1-

5. Let al and be the respective variances of X and Y, and o-j;^ the

covariance of X and K then

+ y = + + 2(7

o-^j_„ = al + — 2a^y

If A' and y are independent, a^y = 0, then

O-l + V = O-'i + 0-y = O-^-i/ (2-8)

Since the variance of a constant is zero, we have

c7L.. = aV.

(2-9)

In particular, if X and Y are independent and normally distributed, then

aX + bY is normally distributed with limiting mean am^ btriy and

variance a^a'i +

For measurement situations in general, metrologists usually strive to

get measurements that are independent, or can be assumed to be independent. The case when two quantities are dependent because both are functions of other measured quantities will be treated under propagation of

error formulas (see Eq. 2-13).

6. Standard errors of the sample mean and the weighted means (of

independent measurements) are special cases of the above. Since

X = (l/«) 2 and the Xj's are independent with variance a^., it follows,

by (2-9), that

'^• = (i)< + (4)'<+---(T)< = ^

(2-10)

as previously stated.

15
If Xi is an average of k values, and is an average of n values, then for

the over-all average, x. it is logical to compute

= _ -^1 + • • • 4" -^A- + -yt+i + • • • + x^+n

k + n and (7= — oi/(A' + n). However, this is equivalent to a weighted mean of

x, and Xo, where the weights are proportional to the number of measurements

in each average, i.e.,

H'l = k. M', = n and

Since

^1 + n + k

W2

the weighting factors vv, and Ho are therefore also inversely proportional

to the respective variances of the averages. This principle can be extended

to more than two variables in the following manner.

Let X|,x.2, . . . , -T^- be a set of averages estimating the same quantity.

The over-all average may be computed to be

X

1

VV, + H'o +

W.2X2 +

where

The variance of .v is, by (2-9).

1 (2-

In practice, the estimated variances s\ will have to be used in the above

formulas, and consequently the equations hold only as approximations.

Propagation of error formulas. The results of a measurement process can usually be expressed by a number of averages x, y, . . . , and the standard

errors of these averages Sj. = 5j;/V^, 5,, = Sy/^yi<, etc. These results, however,

may not be of direct interest; the quantity of interest is in the functional

relationship w„, = /(m_c, w„). It is desired to estimate by m> = f{x,y) and

to compute s,r, as an estimate of cr^.

If the errors of measurements of these quantities are small in comparison

with the values measured, the propagation of error formulas usually work

surprisingly well. The a%, al, and <r^ that are used in the following formulas

will often be replaced in practice by the computed values ^l;, s'i, and s'l. The general formula for a% is given by

"en, + 2 [3/1

dx^ \dy\ \dx\ \dy\

(2-12)

where the partial derivatives in square brackets are to be evaluated at the

averages of .v and y. If X and Y are independent, p = 0 and therefore the

last term equals zero. If A' and l^are measured in pairs, s^^i (Eq.2-4) can be

used as an estimate of Pi^cj^Uy.

16

1
If is functionally related to U and K by

and both U and V are functionally related to X and Y by

mu = gim^, my)

m, = h{m^, niy)

then U and K are functionally related. We will need the covariance

puv(^u(^v to calculate o-'^^. The covariance u^i, is given approximately by

(Tin, = dh~ dh~

' dx_ Idy

8h~

+

dx ]

' dy_ Idy' dXj

(2-13)

The square brackets mean, as before, that the partial derivatives are to be

evaluated at x and y. If X and Y are independent, the last term again

vanishes.

These formulas can be extended to three or more variables if necessary. For convenience, a few special formulas for commonly encountered functions

are listed in Table 2-3 with X, Y assumed to be independent. These may be

derived from the above formulas as exercises.

Table 2-3. Propagation of error formulas for some simple functions

(A" and K are assumed to be independent.)

Function form Approximate formula for s%

Atrij. + Bnty

my

irix + m — "' '-^

*m^, = m^njy

*m„. = m\

m„, = V Wj.

*m„. = In

*m„. = km^nfy

*m„.

H/ 100^ (= coefficient X of variation)

A'-s\ + B'-s'l

(f)'(! + 1)

3.

.-A

(1 + xr

4x'-s-j.

4 .Y

Xw- (not directly derived from

2{n — 1) the formulasit

*Distribution of w is highly skewed and normal approximation could be seriously

in error for small n. tSee, for example. Statistical Theory with Engineering Applications, by A. Hald, John

Wiley & Sons. Inc., New York, 1952. p. 301.

17
In these formulas, if (a) the partial derivatives when evaluated at the averages are small, and

(b) o-j., Gy are small compared to y, then the approximations are good and w tends to be distributed normally

(the ones marked by asterisks are highly skewed and normal approximation

could be seriously in error for small n). Pooling Estimates of Variances. The problem often arises that there are

several estimates of a common variance a'- which we wish to combine into a single estimate. For example, a gage block may be compared with the

master block times, resulting in an estimate of the variance s\. Another

gage block compared with the master block times, giving rise to si, etc. As long as the nominal thicknesses of these blocks are within a certain

range, the precision of calibration can be expected to remain the same. To get a better evaluation of the precision of the calibration process, we

would wish to combine these estimates. The rule is to combine the computed

variances weighted by their respective degrees of freedom, or

y2 ^ i^iS] + v^sl -I • • • -f v^sl (2-14)

The pooled estimate of the standard deviation, of course, is v^^^ ~ In the example, f, = - I, = "a " U • • , = ~ U thus the

expression reduces to

., _(«,-! )s] 1- («2 - 1)4 f • • I- (n, - 1)4 (2-15)

The degrees of freedom for the pooled estimate is the sum of the degrees

of freedom of individual estimates, or ^'| + + • • • i'k = «i + + • • • + til, — k. With the increased number of degrees of freedom, is a more

dependable estimate of (x than an individual s. Eventually, we may consider

the value of s,, to be equal to that of a and claim that we know the precision

of the measuring process. For the special case where k sets of duplicate measurements are available,

the above formula reduces to: 4 = ^ X (2-16)

where difTcrence of duplicate readings. The pooled standard deviation

Su has A degrees of freedom.

For sets of normally distributed measurements where the number of

measurements in each set is small, say less than ten, an estimate of the

standard deviation can be obtained by multiplying the range of these measurements by a constant. Table 2-4 lists these constants corresponding to the

number n of measurements in the set. For large n, considerable information

is lost and this procedure is not recommended.

If there are k sets of n measurements each, the average range R can be

computed. The standard deviation can be estimated by multiplying the

average range by the factor for n.

18
Table 2-4. Estimate of a from the range

n Multiplying factor

2 0.886

3 0.591 4 0.486

5 0.430

6 0.395

7 0.370

8 0.351 9 0.337

10 0.325

*Adapted from Biometrika Tables for Statisticians, Vol. I, edited by E. S. Pearson and H. O. Hartley, The University Press, Cambridge, 1958.

Component of Variance Between Groups. In pooling estimates of vari- ances from a number of subgroups, we have increased confidence in the value

of the estimate obtained. Let us call this estimate the within-group standard

deviation, a^. The within-group standard deviation a^, is a proper measure

of dispersions of values within the same group, but not necessarily the

proper one for dispersions of values belonging to different groups.

If in making calibrations there is a difference between groups, say from

day to day, or from set to set, then the limiting means of the groups are not equal. These limiting means may be thought of as individual measurements; thus, it could be assumed that the average of these limiting means

will approach a limit which can be called the limiting mean for all the groups.

In estimating al.. the differences of individuals from the respective group means are used. Obviously (t^ does not include the differences between

groups. Let us use al to denote the variance corresponding to the differences

between groups, i.e., the measure of dispersion of the limiting means of the

respective groups about the limiting mean for all groups.

Thus for each individual measurement x, the variance of X has two

components, and

9 2 t 2 O" = O"^ + O-u, For the group mean x with n measurements in the group,

n

If k groups of n measurements are available giving averages X|,Xo, . . . ,

X*., then an estimate of a'j. is

I " si = 7 r D (X, - Xf

K — 111

with k I degrees of freedom, where x is the average of all nk measurements.

The resolution of the total variance into components attributable to

identifiable causes or factors and the estimation of such components of

variances are topics treated under analysis of variance and experimental

design. For selected treatments and examples see references 5, 6, and 8.

19
Comparison of Means and Variances

Comparison of means is perhaps one of the most frequently used techniques in metrology. The mean obtained from one measurement process

may be compared with a standard value; two series of measurements on

the same quantity may be compared; or sets of measurements on more than

two quantities may be compared to determine homogeneity of the group

of means.

It is to be borne in mind in all of the comparisons discussed below,

that we are interested in comparing the limiting means. The sample means

and the computed standard errors are used to calculate confidence limits on the difference between two means. The statistic derived from normal

distribution theory is used in this procedure since we are assuming either

the measurement process is normal, or the sample averages are approximately normally distributed.

Comparison of a Mean with a Standard Value. In calibration of

weights at the National Bureau of Standards, the weights to be calibrated are intercompared with sets of standard weights having "accepted" corrections.

Accepted corrections are based on years of experience and considered to be

exact to the accuracy required. For instance, the accepted correction for the

NB'IO gram weight is —0.4040 mg.

The NB'IO is treated as an unknown and calibrated with each set of

weights tested using an intercomparison scheme based on a 100-gm standard

weight. Hence the observed correction for NB'IO can be computed for each

particular calibration. Table 2-5 lists eleven observed corrections of NB'IO

during May 1963.

Calculated 95 percent confidence limits from the eleven observed corrections are —0.4041 and —0.3995. These values include the accepted value

of —0.4040, and we conclude that the observed corrections agree with the

accepted value.

What if the computed confidence Hmits for the observed correction do

not cover the accepted value? Three explanations may be suggested:

1. The accepted value is correct. However, in choosing <x ^= 0.05, we

know that 5 percent of the time in the long run we will make an

error in our statement. By chance alone, it is possible that this particular set of limits would not cover the accepted value.

2. The average of the observed corrections does not agree with the

accepted value because of certain systematic error, temporary or

seasonal, particular to one or several members of this set of data for

which no adjustment has been made.

3. The accepted value is incorrect, e.g., the mass of the standard has

changed.

In our example, we would be extremely reluctant to agree to the third

explanation since we have much more confidence in the accepted value than

the value based only on eleven calibrations. We are warned that something

may have gone wrong, but not unduly alarmed since such an event will happen purely by chance about once every twenty times.

The control chart for mean with known value, to be discussed in a following section, would be the proper tool to use to monitor the constancy

of the correction of the standard mass.

20
Table 2-5. Computation of confidence limits for observed corrections, NB'IO gm *

Date / Xi Observed Corrections to standard 10 gm wt in mg

5-1-63 1 -0.4008

5-1-63 2 -0.4053

5-1-63 3 -0.4022

5-2-63 4 -0.4075

5-2-63 5 -0.3994

5-3-63 6 -0.3986

5-6-63 7 -0.4015

5-6-63 8 -0.3992

' 5-6-63 9 -0.3973

5-7-63 10 -0.4071

5-7-63 11 -0.4012

2 = -4.4201 2 -^f = 1-''7623417 X = -0.40183 mg ^ J. 7761 1673

n

difference = 0.00011744

5- = -i—i-(0.0001 1744) = 0.00001 1744

n — 1 5 = 0.00343 = computed standard deviation of an observed correction about the mean. = 0.00103 = computed standard deviation of the mean of eleven corrections. = computed standard error of the mean.

For a two-sided 95 percent confidence interval for the mean of the above sample of

size 11, a/2 ^ 0.025, i' =^ 10, and the corresponding value of / is equal to 2.228 in the

table of t distribution. Therefore,

L; = X - /-^ = -0.40183 - 2.228 0.00103 = -0.40412

V n and

L„ - .V + = -0.40183 f 2.228 0.00103 =-0.39954

*Data supplied by Robert Raybold, Metrology Division, National Bureau of Standards.

Comparison Among Two or More Means. The difiference between two

quantities X and Y to be measured is the quantity

and is estimated by x — y, where x and y are averages of a number of

measurements of X and Y respectively.

Suppose we are interested in knowing whether the difference m,^,, could

be zero. This problem can be solved by a technique previously introduced,

i.e., the confidence limits can be computed for and if the upper and

lower limits include zero, we could conclude that /h^ « may take the value

zero; otherwise, we conclude that the evidence is against 0. Let us assume that measurements of X and Y are independent with

known variances a\ and a\ respectively.

By Eq. (2.10)

a\ = — for X of n measurements

n

a'l = ^ for y of k measurements

then by (2.8),

21
Therefore, the quantity

= ^ 4_ ^

n ^ k - - 0

n k

(2-17)

is approximately normally distributed with mean zero and a standard

deviation of one under the assumption m^_^ = 0. If o-j and Gy are not known, but the two can be assumed to be approximately equal, e.g., x and y are measured by the same process, then s\ and

$1 can be pooled by Eq. (2-15), or

0 _ {n - 1)5| + {k- \)sl n + k-2

This pooled computed variance estimates

2 2 2 O" = = (Jy so that

n ^ k " nk

Thus, the quantity

, = <^) - «

(2-18)

•5,

is distributed as Student's "r", and a confidence interval can be set about

mj._y with V = n ^ k ^ 2 and /? = 1 — cc. If this interval does not include

zero, we may conclude that the evidence is strongly against the hypothesis = my.

As an example, we continue with the calibration of weights with

NB'lOgm. For II subsequent observed corrections during September and

October, the confidence interval (computed in the same manner as in the

preceding example) has been found to be = -0.40782

L„ = -0.40126

Also,

y = -0.40454 and -4= = 0.00147

k

It is desired to compare the means of observed corrections for the two sets of data. Here

n = k = \\

X = -0.40183, y = -0.40454

0 = 0.000011669, 4 - 0.000023813 = ^(0.000035482) = 0.000017741

n + k 11+11 2

nk 121 11

nk " A/ 11 " + = X 0.000017741 = 0.00180

22
For all = 0.025, I - a = 0.95, and v = 20, t = 2.086. Therefore,

Lu = ix - y) + r^^?-^ Sp = 0.00271 + 2.086 / 0.00180 = 0.00646

L, = ix -y) - t^~f^ s, = -0.00104

Since L, < 0 < shows that the confidence interval includes zero, we

conclude that there is no evidence against the hypothesis that the two

observed average corrections are the same, or rrij, = rriy. Note, however,

that we would reach a conclusion of no difference wherever the magnitude

of X — j' (0.00271 mg) is less than the half-width of the confidence interval

(2.086 X 0.00180 = 0.00375 mg) calculated for the particular case. When

the true difference m:,_y is large, the above situation is not likely to happen;

but when the true difference is small, say about 0.003 mg, then it is highly

probable that a conclusion of no difference will still be reached. If a detection

of difference of this magnitude is of interest, more measurements will be needed.

The following additional topics are treated in reference 4.

1. Sample sizes required under certain specified conditions—Tables A-8

and A-9.

2. al cannot be assumed to be equal to al—Section 3-3.1.2.

3. Comparison of several means by Studentized range—Sections 3-4

and 15-4. Comparison of variances or ranges. As we have seen, the precision of

a measurement process can be expressed in terms of the computed standard

deviation, the variance, or the range. To compare the precision of two

processes a and b, any of the three measures can be used, depending on

the preference and convenience of the user. Let s'i be the estimate of with degrees of freedom, and si be the

estimate of a'l with Vf, degrees of freedom. The ratio F= si/sl has a distri- bution depending on and I'j,. Tables of upper percentage points of F

are given in most statistical textbooks, e.g., reference 4, Table A-5 and

Section 4-2. In the comparison of means, we were interested in finding out if the

absolute difference between and w,, could reasonably be zero; similarly,

here we may be interested in whether a'i = a'l, or a'i/al = 1. In practice,

however, we are usually concerned with whether the imprecision of one

process exceeds that of another process. We could, therefore, compute the

ratio of si to 4, and the question arises: If in fact al a'l what is the

probability of getting a value of the ratio as large as the one observed?

For each pair of values of Va and v^, the tables list the values of F which are exceeded with probability a, the upper percentage point of the distribution

of F. If the computed value of F exceeds this tabulated value of F„ „^ ^^, then we conclude that the evidence is against the hypothesis al = ay, if it

is less, we conclude that al could be equal to a'l. For example, we could compute the ratio of si to s\ in the preceding

two examples.

Here the degrees of freedom Vy = Vj, = 10, the tabulated value of F

which is exceeded 5 percent of the time for these degrees of freedom is 2.98, and

5[ _ 0.000023813 ^ 2 041

si ~ 0.000011669 "

23
Since 2.04 is less than 2.98, we conclude that there is no reason to believe

that the precision of the calibration process in September and October is poorer than that of May.

For small degrees of freedom, the critical value of F is rather large,

e.g., for Va I't, = 3, and a' - 0.05, the value of F is 9.28. It follows

that a small difference between cr'^ and al is not likely to be detected with a small number of measurements from each process. The table below gives

the approximate number of measurements required to have a four-outof-five chance of detecting whether is the indicated multiple of (while

maintaining at 0.05 the probability of incorrectly concluding that > a„, when in fact aa = a,j).

Table A-1 1 in reference 4 gives the critical values of the ratios of ranges, and Tables A-20 and A-21 give confidence limits on the standard deviation

of the process based on computed standard deviation.

Control Charts Technique for

Maintaining Stability and Precision

A laboratory which performs routine measurement or calibration operations yields, as its daily product, numbers—averages, standard deviations,

and ranges. The control chart techniques therefore could be applied to these

numbers as products of a manufacturing process to furnish graphical

evidence on whether the measurement process is in statistical control or out

of statistical control. If it is out of control, these charts usually also indicate

where and when the trouble occurred.

Control Chart for Averages. The basic concept of a control chart is in accord with what has been discussed thus far. A measurement process

with limiting mean m and standard deviation a is assumed. The sequence

of numbers produced is divided into "rational" subgroups, e.g., by day,

by a set of calibrations, etc. The averages of these subgroups are computed.

These averages will have a mean m and a standard deviation o-/v^ where

n is the number of measurements within each subgroup. These averages

are approximately normally distributed. In the construction of the control chart for averages, m is plotted as the center line, m + k(a/\/^) and in — A'(c7/V^) are plotted as control limits,

and the averages are plotted in an orderly sequence. If k is taken to be 3, we know that the chance of a plotted point falling outside of the limits,

if the process is in control, is very small. Therefore, if a plotted point falls outside these limits, a warning is sounded and investigative action to locate

the "assignable" cause that produced the departure, or corrective measures,

are called for. The above reasoning would be applicable to actual cases only if we have

chosen the proper standard deviation a. If the standard deviation is estimated

by pooling the estimates computed from each subgroup and denoted by

(within group), obviously differences, if any, between group averages have

Multiple No. of measurements

1.5 2.0

2.5

3.0

3.5 4.0

39

159

7

6

5

24
not been taken into consideration. Where there are between-group diflFerences the variance of the individual x is not aljn, but, as we have seen before,

o"* + (o"L/«), where al represents the variance due to differences between

groups. If (xl is of any consequence as compared to al,, many of the x values

would exceed the limits constructed by using cr^ alone.

Two alternatives are open to us: (1) remove the cause of the betweengroup variation; or, (2) if such variation is a proper component of error, take it into account as has been previously discussed.

As an illustration of the use of a control chart on averages, we use again

the NB'IO gram data. One hundred observed corrections for NB'IO are

plotted in Fig. 2-5, including the two sets of data given under comparison

of means (points 18 through 28, and points 60 through 71). A three-sigma

hmit of 8.6 /ig was used based on the "accepted" value of standard deviation.

We note that all the averages are within the control limits, excepting

numbers 36, 47, 63, 85, and 87. Five in a hundred falling outside of the

three-sigma limits is more than predicted by the theory. No particular reasons, however, could be found for these departures.

Since the accepted value of the standard deviation was obtained by

pooling a large number of computed standard deviations for within-sets of

calibrations, the graph indicates that a "between-set" component may be

present. A slight shift upwards is also noted between the first 30 points and

the remainder.

<

cr. o

ano

420.0

T T

O INDICATES CALIBRATIONS WITH COMPUTED

STANDARD DEVIATIONS OUT OF CONTROL, WEIGHTS RECALIBRATED.

LOWER LIMIT=-4I2.6 (3-SIGMA). z -410.01- Oo -4 04.0

LJ

cr. -400.0

<ro

o^ -390.0

(T

LlI CO

CDO

• . •• .• • • • . •

UPPER LIMIT=-395.4(3-SIGMA)

10 20 30 40 50 60

NUMBER OF SAMPLES

70 80 90 100

Fig. 2-5. Control chart on x for NB'IO gram.

Control Chart for Standard Deviations. The computed standard

deviation, as previously stated, is a measure of imprecision. For a set of

calibrations, however, the number of measurements is usually small, and

consequently also the degrees of freedom. These computed standard deviations with few degrees of freedom can vary considerably by chance alone, even though the precision of the process remains unchanged. The control

chart on the computed standard deviations (or ranges) is therefore an indispensable tool. The distribution of s depends on the degrees of freedom associated with

it, and is not symmetrical about iris. The frequency curve of s is limited on the

left side by zero, and has a long "tail" to the right. The limits, therefore,

25
are not symmetrical about m^. Furthermore, if the standard deviation of

the process is known to be a, is not equal to a, but is equal to c^cr, where

C.2 is a constant associated with the degrees of freedom in s. The constants necessary for the construction of three-sigma control

limits for averages, computed standard deviations, and ranges, are given

in most textbooks on quality control. Section 18-3 of reference 4 gives

such a table. A more comprehensive treatment on control charts is given

in ASTM "Manual on Quality Control of Materials," Special Technical

Publication 15-C.

Unfortunately, the notation employed in quality control work differs

in some respect from what is now standard in statistics, and correction

factors have to be applied to some of these constants when the computed

standard deviation is calculated by the definition given in this chapter.

These corrections are explained in the footnote under the table. As an example of the use of control charts on the precision of a cali- bration process, we will use data from NBS calibration of standard cells.* Standard cells in groups of four or six are usually compared with an NBS

standard cell on ten separate days. A typical data sheet for a group of

six cells, after all the necessary corrections, appears in Table 2-6. The stan- dard deviation of a comparison is calculated from the ten comparisons for

each cell and the standard deviation for the average value of the ten comparisons is listed in the line marked SDA. These values were plotted as points 6 through 1 1 in Fig. 2-6.

(393)

:

A ( 334)

32 CELLS CALIBRATED

UPPER LIMIT = .190( 3-SIGMA) < 2 >

UJa

CENTER LINE= .111

O h LOWER LIMIT=.031 (3-SIGMA) ^ 0 CELL CALIBRATIONS

Fig. 2-6. Control chart on s for the calibration of standard cells.

Let us assume that the precision of the calibration process remains the same. We can therefore pool the standard deviations computed for each

cell (with nine degrees of freedom) over a number of cells and take this value as the current value of the standard deviation of a comparison, a. The corresponding current value of standard deviation of the average of

ten comparisons will be denoted by a' = o-/a/TO. The control chart will be

made on s' = s/\/\0.

*Illustrative data supplied by Miss Catherine Law, Electricity Division, National Bureau of Standards.

26
For example, the SDA's for 32 cells calibrated between June 29 and

August 8, 1962, are plotted as the first 32 points in Fig. 2-6. The pooled

standard deviation of the average is 0.114 with 288 degrees of freedom. The

between-group component is assumed to be negligible.

Table 2-6. Calibration data for six standard cells

Day Corrected Emf's and standard deviations, Microvolts

] 27.10 24.30 31.30 33.30 32.30 2 25.96 24.06 31.06 34.16 33.26 23.76

3 26.02 24.22 31.92 33.82 33.22 24.02 4 26.26 24.96 31.26 33.96 33.26 24.16

5 27.23 25.23 31.53 34.73 33.33 24.43 6 25.90 Z4.4U 31.80 33.90 32.90 24.10

7 26.79 24.99 32.19 34.39 33.39 24.39

8 26.18 24.98 32.18 35.08 33.98 24.38 9 26.17 25.07 31.97 34.27 33.07 23.97

10 26.16 25.16 31.96 34.06 32.96 24.16 R 1.331 1.169 1.127 1.777 1.677 1.233 AVG . 26.378 24.738 31.718 34.168 33.168 24.058 SD 0.482 0.439 0.402 0.495 0.425 0.366 SDA 0.153 0.139 0.127 0.157 0.134 0.116

Position Emf, volts Position Emf, volts

1 1.0182264 4 1.0182342 2 1.0182247 5 1.0182332

3 1.0182317 6 1.0182240

Since n = 10, we find our constants for three-sigma control limits on s' in Section 18-3 of reference 4 and apply the corrections as follows:

Center line = J c,a' = 1.054 x 0.9227 x 0.114 = 0.111

V « — 1

Lower limit = J—^^B,(t' = 1.054 x 0.262 x 0.114 = 0.031

V « — 1

Upper limit = y^-^ ^.a' = 1.054 X 1.584 x 0.1 14 = 0.190

The control chart (Fig. 2-6) was constructed using these values of center

line and control limits computed from the 32 calibrations. The standard

deviations of the averages of subsequent calibrations are then plotted.

Three points in Fig. 2-6 far exceed the upper control limit. All three cells, which were from the same source, showed drifts during the period of

calibration. A fourth point barely exceeded the limit. It is to be noted that

the data here were selected to include these three points for purposes of

illustration only, and do not represent the normal sequence of calibrations.

The main function of the chart is to justify the precision statement on

the report of calibration, which is based on a value of a estimated with

perhaps thousands of degrees of freedom and which is shown to be in control.

The report of calibration for these cells (cr = 0.1 17 = 0.12) could read:

"Each value is the mean of ten observations made between

and . Based on a standard deviation of 0.12 microvolts for the means, these values are correct to 0.36 microvolts relative to the

volt as maintained by the national reference group."

27
Linear Relationship and Fitting of

Constants by Least Squares

In using the arithmetic mean of n measurements as an estimate of the

limiting mean, we have, knowingly or unknowingly, fitted a constant to the data by the method of least squares, i.e., we have selected a value m

for m such that

t {yi - my ^td\ 1 I

is a minimum. The solution is m = y. The deviations di = yi — m = yi — y

are called residuals.

Here we can express our measurements in the form of a mathematical

model

y-w + e (2-19)

where Y stands for the observed values, m the limiting mean (a constant),

and e the random error (normal) of measurement with a limiting mean zero and a standard deviation a. By (2-1) and (2-9), it follows that

my = m -\- m, = m

and

2 2 al = a' The method of least squares requires us to use that estimator m for m such

that the sum of squares of the residuals is a minimum (among all possible

estimators). As a corollary, the method also states that the sum of squares

of residuals divided by the number of measurements n less the number of

estimated constants p will give us an estimate of (j% i.e.,

^2 ^ ILiyi- mf _ S iyi - y f (2-20) n — p n — \

It is seen that the above agrees with our definition of 5\

Suppose K, the quantity measured, exhibits a linear functional relationship with a variable which can be controlled accurately; then a model can

be written as Y ^ a + bX + € (2-21)

where, as before, Y is the quantity measured, a (the intercept) and b (the

slope) are two constants to be estimated, and e the random error with

limiting mean zero and variance We set X at .Vi, and observe j,. For

example, yi might be the change in length of a gage block steel observed for n equally spaced temperatures Xi within a certain range. The quantity of

interest is the coefficient of thermal expansion b. For any estimates of a and b, say a and b, we can compute a value v'i for each Xi, or

f'j = d + bx.

If we require the sum of squares of the residuals

n

i = l to be a minimum, then it can be shown that

. i (Xi - x){y, - y)

b = (2-22)

28
and

d=y -bx (2-23)

The variance of Y can be estimated by

with n — 2 degrees of freedom since two constants have been estimated

from the data.

The standard errors of b and d are respectively estimated by and s^, where

2 {X, - xf

With these estimates and the degrees of freedom associated with 5% confidence limits can be computed for d and b for the confidence coefficient

selected if we assume that errors are normally distributed.

Thus, the lower and upper limits of a and b, respectively, are:

d — ts^, d + ts^

b — tsi, b + tsf,

for the value of t corresponding to the degree of freedom and the selected

confidence coefficient. The following problems relating to a linear relationship between two

variables are treated in reference 4, Section 5-4.

1. Confidence intervals for a point on the fitted line.

2. Confidence band for the line as a whole.

3. Confidence interval for a single predicted value of Y for a given X.

Polynomial and multivariate relationships are treated in Chapter 6 of

the same reference.

REFERENCES

The following references are recommended for topics introduced in the first section

of this chapter:

1. Wilson, Jr., E. B., An Introduction to Scientific Research, McGraw-Hill Book

Company, New York, 1952, Chapters 7, 8, and 9.

2. Eisenhart, Churchill, "Realistic Evaluation of the Precision and Accuracy of Instrument Calibration System," Journal of Research of the National Bureau

of Standards, Vol. 67C, No. 2, 1963.

3. Youden, W. J., Experimentation and Measurement, National Science Teacher

Association Vista of Science Series No. 2, Scholastic Book Series, New York.

In addition to the three general references given above, the following are selected with special emphasis on their ease of understanding and applicability in the

measurement science:

29
Statistical Methods

4. Natrella, M. G., Experimental Statistics, NBS Handbook 91, U.S. Government

Printing Office, Washington, D.C., 1963.

5. Youden, W. J., Statistical Methods for Chemists, John Wiley & Sons, Inc., New York, 1951.

6. Davies, O. L., Statistical Method in Research and Production (3rd ed.), Hafner

Publishing Co., Inc., New York, 1957. Textbooks

7. Dixon, W. J. and F. J. Massey, Introduction to Statistical Analysis (2nd ed.), McGraw-Hill Book Company, New York, 1957.

8. Brownlee, K. A., Statistical Theory and Methodology in Science and Engineering,

John Wiley & Sons, Inc., New York, 1960.

9. Areley, N. and K. R. Buch, Introduction to the Theory of Probability and Statistics, John Wiley & Sons, Inc., New York, 1950.

Additional Books on Treatment of Data

10. American Society for Testing and Materials, Manual on Presentation of Data

and Control Chart Analysis (ST? 15D), 1976, 162 p.

11. American Society for Testing and Materials, ASTM Standard on Precision

and Accuracy for Various Applications , 1977, 249 p.

12. Box, G. E. P., Hunter, W. G., and Hunter, J. S., Statistics for Experimenters, an Introduction to Design, Data Analysis, and Model Building, John Wiley and

Sons, Inc., New York, 1978, 575 p.

13. Cox, D. R., Planning of Experiments , John Wiley and Sons, Inc., New York,

1958, 308 p.

14. Daniel, C. and Wood, F. S., Fitting Equations to Data, Computer Analysis of

Multifactor Data, 2d ed., John WUey and Sons, Inc., New York, 1979, 343 p.

15. Deming, W. E., Statistical Adjustment of Data, John WUey and Sons, Inc., New York, 1943, 261 p.

16. Draper, N. R. and Smith, H., Applied Regression Analysis, John WUey and

Sons, Inc., New York, 1966, 407 p.

17. Himmelblau, D. M., Process Analysis by Statistical Methods, John Wiley and

Sons, Inc., New York, 1970, 464 p.

18. Ku, H. H., ed.. Precision Measurement and Calibration: Statistical Concepts

and Procedures, Natl. Bur. Stand. (U.S.) Spec. Publ. 300, 1969, v.p.

19. Mandel, J., The Statistical Analysis of Experimental Data, Interscience, New

York, 1964, 410 p.

20. Mosteller, F. and Tukey, J. W., Data Analysis and Regression, a Second Course

in Statistics, Addison-Wesley, Reading, Massachusetts, 1977, 588 p.

30
POSTSCRIPT

ON

STATISTICAL GRAPHICS

Over the years since the publication of the above article, it has become

apparent that some additions on recent developments for the treatment of

data may be useful. It is equally apparent that the concepts and techniques

introduced in the original article remain as valid and appropriate as when

first written. For this reason, a few additional sections on statistical graphics

are added as a postscript.

The power of small computers and the associated sophisticated software

have pushed graphics into the forefront. Plots and graphs have always been

popular with engineers and scientists, but their use has been limited by

the time and work involved. Graphics packages now-a-days allow the user

to do plots and graphs wdth ease, and a good statistical package will also

automatically present a number of pertinent plots for examination. As John

Tvikey said, "the greatest value of a picture is when it forces us to notice

what we never expected to see." [l] An outlier? Skewed distribution of

values? Poor modelling? What is the data trying to say? Answers to

all these come naturally through inspection of plots and graphs, whereas

colimins of numbers reveal little, if anything.

Control charts for the mean (Fig. 2-5) and standard deviation (Fig. 2-6) are classical examples of graphical methods. Control charts were introduced

by Walter Shewhart some 60 years ago, yet the technique remains a popular

and most useful tool in business and industry. Simplicity (once constructed),

self-explanatory nature, and robustness (not depending on assumptions) are, and should be, the main desirable attributes of all graphs and plots.

Since statistical graphics is a huge subject, only a few basic techniques

that are particularly useful to the treatment of measurement data will be

discussed, together with references for further reading.

Plots for Summary and Display of Data

Stem and Leaf. The stem and leaf plot is a close relative of the histogram, but it uses digits of data values themselves to show features of the

data set instead of areas of rectangles. First proposed by John W. Tukey,

a stem and leaf plot retains more information from the data than the his- togram and is particularly suited for the display of small to moderate-sized

data sets.

31
Fig. 1 is a stem and leaf plot of 48 measurements of the isotopic ratio

of ^^Bromine to ^^Bromine. Values of these 48 data points, listed in Table

1, range from 1.0261 to 1.0305, or 261 to 305 after coding. The leaves are the last digits of the data values, 0 to 9. The stems are 26, 27, 28, 29, and

30. Thus 261 is split into two parts, plotted as 26 | 1. In this case, because

of the heavy concentration of values in stems 28 and 29, two lines are given

to each stem, with leaves 0 to 4 on the first line, and 5 to 9 on the second.

Stems are shown on the left side of the vertical line and individual leaves on

the right side. There is no need for a separate table of data values - they

are all shown in the plot!

The plot shows a slight skew towards lower values. The smallest value

separates from the next by 0.7 units. Is that an outlier? These data will be

examined again later.

26 1 26. 89

27 034

27. 9

28 00334

28. 566678889

29 001233344444

29. 5666678999

30 0022

30. 5

Fig. 1 . Stem and leaf plot. 48 values of isotopic ratios, bromine (79/81).

Unit = {Y- 1.0) X 10*, thus 26 1 1 = 1.0261.

Table 1. Y—Ratios 79/81 for reference sample

DETERMINATION I DETERMINATION II Instrument #4 Instrtunent #1 Instrument #4 Instrument #1

1.0292 1.0289 1.0296 1.0284

1.0294 1.0285 1.0293 1.0270

1.0298 1.0287 1.0302 1.0279

1.0302 1.0297 1.0305 1.0269

1.0294 1.0290 1.0288 1.0273

1.0296 1.0286 1.0294 1.0261

1.0293 1.0291 1.0299 1.0286

1.0295 1.0293 1.0290 1.0286

1.0300 1.0288 1.0296 1.0293

1.0297 1.0298 1.0299 1.0283

1.0296 1.0274 1.0299 1.0263

1.0294 1.0280 1.0300 1.0280

Ave. 1.029502 1.028792 1.029675 1.027683

.00000086 .00000041 .00000024 .00000069 s .00029 .00064 .00049 .00083 sOy .00008 .00018 .00014 .00024

32
Box Plot. Customarily, a batch of data is summarized by its average

and standard deviation. These two numerical values characterize a nor- mal distribution, as explained in expression (2-0). Certain features of the

data, e.g., skevirness and extreme values, are not reflected in the average and

standard deviation. The box plot (due also to Tukey) presents graphically a five-number summary which, in many cases, shows more of the original

features of the batch of data then the two number summary.

To construct a box plot, the sample of numbers are first ordered from

the smallest to the largest, resulting in

a;(i),X(2),---2:(n)-

Using a set of rules, the median, m, the lower fourth, Fi, and the upper

fourth, Fu, are calculated. By definition, the interval [F^ — Fi) contains half

of all data points. We note that m, F^, and Fi are not disturbed by outliers.

The interval {F^ — Fi) is called the fourth spread. The lower cutoff limit

is

Fi-1.5{F^- Fi)

and the upper cutoff limit is

F„ + 1.5(F, ~ Fi).

A "box" is then constructed between Fi and F^, with the median line

dividing the box into two parts. Two tails from the ends of the box extend

to X(i) and a;(„) respectively. If the tails exceed the cutoff limits, the cutoff

limits are also marked.

From a box plot one can see certain prominent features of a batch of

data:

1. Location - the median, and whether it is in the middle of the box.

2. Spread - The fourth spread (50 percent of data): - lower and upper

cut off limits (99.3 percent of the data will be in the interval if the

distribution is normal and the data set is large).

3. Symmetry /skewness - equal or different tail lengths.

4. Outlying data points - suspected outliers.

33
The 48 measurements of isotopic ratio bromine (79/81) shown in Fig. 1 were actually made on two instruments, with 24 measurements each. Box

plots for instrument I, instrument II, and for both instruments are shown in

Fig. 2.

310

300

290 -

280

270

260

X

X

X

X

X

X

X X(N), LARGEST

X

X

UPPER FOURTH

MEDIAN

LOWER FOURTH

LOWER CUTOFF LIMIT

X(l). SMALLEST

INSTRUMENT INSTRUMENT II COMBINED I k II

Rg. 2. Box plot of isotopic ratio, bromine (79/91).

The five ntmiber summary for the 48 data point is, for the combined data:

Smallest:

Median Xr

Lower Fourth Xi

Upper Fourth X^,:

Largest:

X(l)

m =

Xr

I Xi

u =

X,

X,

X,

261

(n + l)/2 = (48 + l)/2 = 24.5

x^^rn.) if is an integer;

[^[M) + a;(M+i)]/2 if not; where M is the largest integer

not exceeding m.

(291 + 292)/2 = 291.5

(M + l)/2 = (24 + l)/2 = 12.5

if I is an integer; = x(Z + l)]/2 if not,

where L is the largest integer

not exceeding t. (284 + 285)/2 = 284.5 n + l- / = 49 - 12.5 = 36.5

if u is an integer;

[«([/) + a:(i7+i)]/2 if not,

where U is the largest integer

not exceeding u. (296 + 296)/2 = 296

305

34
Box plots for instruments I and II are similarly constructed. It seems

apparent from these two plots that (a) there was a difference between the

results for these two instruments, and (b) the precision of instrument II is better than that of instrviment I. The lowest value of instrument I, 261, is less than the lower cutoff for the plot of the combined data, but it does not

fall below the lower cutoff for instrument I alone. As an exercise, think of

why this is the case. Box plots can be used to compare several batches of data effectively

and easily. Fig. 3 is a box plot of the amount of magnesiimi in different

parts of a long aUoy rod. The specimen number represents the distance, in

meters, from the edge of the 100 meter rod to the place where the specimen was taken. Ten determinations were made at the selected locations for each

specimen. One outlier appears obvious; there is also a mild indication of

decreasing content of magnesium along the rod.

Variations of box plots are given in [3] and [4].

CO*o

EdO

o:

(±3 cu

X:

O

o

COz

<£

LARGEST

R FOURTH

R FOURTH

SMALLEST

BARl BARS BAR20 BAR50 BAR85

Fig. 3. Magnesium content of specimens taken.

Plots for Checking on Models and Assumptions

In making measurements, we may consider that each measiirement is made up of two parts, one fixed and one variable, i.e.,

Meastirement = fixed part + variable part,

or, in other words.

Data = model + error.

We use measured data to estimate the fixed part, (the Mean, for ex- ample), and use the variable part (perhaps summarized by the standard

deviation) to assess the goodness of our estimate.

35
Residuals. Let the ith data point be denoted by y^, let the fixed part

be a constant A/, and let the random error be €{ as used in equation (2-19).

Then

y, = M + i = l,2,...,n.

If we use the method of least squares to estimate m, the resulting esti- mate is

or the average of all measurements.

The ith residual, Tj, is defined as the difference between the ith data

point and the fitted constant, i.e.

r^ = Vi - yIn general, the fixed part can be a function of another variable X (or more than one variable). Then the model is

yi = F(X,) 4-

and the ith residual is defined as

yz - F{x^),

where F{xj) is the value of the function computed with the fitted parameters.

K the relationship between F and X is linear as in (2-21), then = y^ —

(a -|- bxi) where a and b are the intercept and the slope of the fitted straight

line, respectively.

When, as in calibration work, the values of F{xi) are frequently considered to be known, the differences between measured values and known values

will be denoted c/j, the i th deviation, and can be used for plots instead of

residuals.

Adequacy of Model. Following is a discussion of some of the issues

involved in checking the adequacy of models and assumptions. For each

issue, pertinent graphical techniques involving residuals or deviations are presented.

In calibrating a load cell, known deadweights are added in sequence and

the deflections are read after each additional load. The deflections are plotted against loads in Fig. 4. A straight line model looks plausible, i.e.,

(deflection z) = bo + 6i(loadi).

A line is fitted by the method of least squares and the residuals from the

fit are plotted in Fig. 5. The parabolic curve suggests that this model is inadequate, and that a second degree equation might fit better:

(deflection^) - bo + fei(loadi) + 62(loadj)^.

36
LOAD CELL CALIBRATION

o

I—o

2.5

1 .5 -

0.5

Fig. 4. Plot of deflection vs load.

LOAD CELL CALIBRATION

300

0.003

0.002 -

0.001 -

0 -

I <

O-0.001 H

(/I UJ -0.002

-0.003 -

-0.004 -

-0.005

0

^ M

^

X X

50 1 00 1 50

LOAD

200

X

X

250 300

Rg. 5. Plot of residuals after linear fit

37
This is done and the residuals from this second degree model are plotted against loads, resulting in Fig. 6. These residuals look random, yet a

pattern may still be discerned upon close inspection. These patterns can be investigated to see if they are peculiar to this individual load cell, or are common to all load cells of similar design, or to all load cells. Uncertainties based on residuals resulting from an inadequate model

could be incorrect and misleading.

LOAD CELL CALIBRATION

0 0006

0 0004

0,0002 H

i/i

i <c^ 0

UJ

ct-0.0002 H

0.0004 -

-0.0006

X X

X X

X

X X

50 00

~1 '

\ '

1

150 200 250

LOAD

Fig. 6. Plot of residuals after quadratic fit.

300

Testing of Underlying Assumptions. In equation (2-19),

Y = m + €,

the assumptions are made that c represents the random error (normal) and

has a limiting mean zero and a standard deviation a. In many measurement

situations, these assimiptions are approximately true. Departures from these

assumptions, however, would invalidate our model and our assessment of

uncertainties. Residual plots help in detecting any unacceptable departures

from these assumptions.

Residuals from a straight line fit of measured depths of weld defects (radiographic method) to known depths (actually measured) are plotted against

the known depths in Fig. 7. The increase in variability with depths of defects is apparent from the figure. Hence the assumption of constant cr over

the range of F{x) is violated. If the variability of residuals is proportional

to depth, fitting of ln(?/j) against known depths is suggested by this plot.

The assumption that errors are normaUy distributed may be checked

by doing a normal probability plot of the residuals. If the distribution is approximately normal, the plot should show a linear relationship. Curvature

in the plot provides evidence that the distribution of errors is other than

38
ALASKA PIPELINE RADIOGRAPHIC DEEECT BIAS CURVE

0 20 30 40 50 60

TRUE DEPTH (IN ,001 INCHES)

r

70 80 90

Rg. 7. Plot of residuals after linear fit. Measured depth of weld defects vs true depth.

LOAD CELL CALIBRATION

0 0006 -T

0 0004 - X

^

XX

-0.0004 - X

X

-0.0006 -| 11

1

1

1

1 <1 .1

. -3-2-10 1 2 3 LOAD

Rg. 8. Normal probability plot of residuals after quadratic fit.

normal. Fig. 8 is a normal probability plot of the residuals in Fig. 6,

showing some evidence of departure from iioriTiality. Note the change in slope in the middle range.

Inspection of normal probability plots is not an easy job, however, unless

the curvature is substantial. Frequently synunetry of the distribution of

39
errors is of main concern. Then a stern and leaf plot of data or residuals serves the purpose just as well as, if not better than, a normal probability

plot. See, for example, Fig. 1.

Stability of a Measurement Sequence. It is a practice of most

experimenters to plot the results of each run in sequence to check whether

the measurements are stable over runs. Tlie run- sequence plot differs from

control charts in that no formal rules are used for action. The stability of a measurement process depends on many factors that are recorded but are not

considered in the model because their effects are thought to be negligible.

Plots of residuals versus days, sets, instruments, operators, temperatures, humidities, etc., may be used to check whether effects of these factors are indeed negligible. Shifts in levels between days or instruments (see Fig.

2), trends over time, and dependence on environmentaJ conditions are easily seen from a plot of residuals versus such factors. In calibration work, frequently the values of standards are considered to be known. The differences between measured values and known values may

be used for a plot instead of residuals.

Figs. 9, 10, and 11 are multi-trace plots of results from three laboratories of measuring linewidth standards using different optical imaging

methods. The difference of 10 measured line widths from NBS values are plotted against NBS values for 7 days. It is apparent that measurements

made on day 5 were out of control in Fig. 9. Fig. 10 shows a downward

trend of differences with increasing line widths; Fig. 11 shows three significant outliers. These plots could be of help to those laboratories in locating

and correcting causes of these anomalies. Fig. 12 plots the results of cal- ibration of standard watt- hour meters from 1978 to 1982. It is evident

that the variability of results at one time, represented by (discussed under Component of Variance Between Groups, p. 19), does not reflect the

variability over a period of time, represented by Uf, (discussed in the same

section). Hence, three measurements every three months would yield better

variability information than, say, twelve measurements a year apart.

Rg. 9. Differences of linewidth measurements from NBS values. Measurements on day 5 inconsistent with others— Lab A.

40
-e.s

-1 . • -

1 .s

HIS ^XkLUCS

Rg. 10. Trend with iccreasing linewidths— Lab B.

0.75

4 0 6.0 6.0

»«8S VALUf S l^ml

12.0

Rg. 1 1 . Significant isolated outliers— Lab C.

41
100.06

100.04 —

100.02 -

100.00 -

99.98

« • CM-iemTioNS oc tem mm

t • CM-ISMTIONS T>«E£ KMTm APtWT

X

X

X

T

Fig

1978 1979 1980 1981 1962 1983

12. Measurements (% reg) on the power standard at 1-year and 3-month intervals.

Concluding Remarks

About 25 years ago, John W. Tukey pioneered "Exploratory Data Analysis" [l], and developed methods to probe for information that is present in

data, prior to the application of conventional statistical techniques. Naturally graphs and plots become one of the indispensable tools. Some of these

techniques, such as stem and leaf plots, box plots, and residual plots, are

briefly described in the above paragraphs. References [l] through [5] cover most of the recent work done in this area. Reference [7] gives an up- to-date

bibliography on Statistical Graphics.

Many of the examples used were obtained through the use of DATAPLOT [6]. I wish to express my thanks to Dr. J. J. Filliben, developer of

this software system. Thanks are also due to M. Carroll Croarkin for the use

of Figs. 9 thru 12, Susannah Schiller for Figs. 2 and 3 and Shirley Bremer

for editing and typesetting.

References

[1] Tukey, John W., Exploratory Data Analysis, A ddision- Wesley, 1977.

[2] Cleveland, William S., The Elements of Graphing Data, Wadsworth

Advanced Book and Software, 1985.

[3] Chambers, J.M., Cleveland, W. S., Kleiner, B., and Tukey, P. A.,

Graphical Methods for Data Analysis, Wadsworth International Group

and Duxbury Press, 1983.

42
[4] Hoaglin, David C, Mosteller, Frederick, and Tukey, John W., Understanding Robust and Exploratory Data Analysis, John Wiley & Sons,

1983.

[5] Velleman, Paul F., and Hoaglin, David C, Applications, Basics, and

Computing of Exploratory Data Analysis, Duxbnry Press, 1981.

[6] FiUiben, James J., 'DATAPLOT - An Interactive High-level Language

for Graphics, Nonlinear Fitting, Data Analysis and Mathematics,'

Computer Graphics, Vol. 15, No. 3, August, 1981.

[7] Cleveland, William S., et al., 'Research in Statistical Graphics,' Journal

of the American Statistical Association, Vol. 82, No. 398, June 1987,

pp. 419-423.

43
NBS-114A (REV. 2-8C) U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

SHEET (See instructions)

1. PUBLICATION OR

REPORT NO.

NBS/SP-747

2. Performing Organ. Report No. 3. Publication Date August 1988

4. TITLE AND SUBTITLE

Statistical Concepts in Metrology

Statistical Graphics — With a Postscript on

5. AUTHOR(S)

Harry H. Ku

6. PERFORMING ORGANIZATION (If joint or other than NBS. see instructions) 7. Contract/Grant No. NATIONAL BUREAU OF STANDARDS

U.S. DEPARTMENT OF COMMERCE

GAITHERSBURG, MD 20899

8. Type of Report & Period Covered

Final

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street, City. State, ZIP)

Same as item 6.

10. SUPPLEMENTARY NOTES

Library of Congress Catalog Card Number: 88-600569

Document describes a computer program; SF-185, FlPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most si gnificant information. If document includes a si gnificant

bibliography or literature survey, mention it here)

"Statistical Concepts in Metrology" was originally written as Chapter 2

for the Handbook of Industrial Metrology published by the American Society of Tool and Manufacturing Engineers, 1967. It was reprinted as one of 40

papers in NBS Special Publication 300, Volume I, Precision Measurement and

Calibration; Statistical Concepts and Procedures, 1969. Since then this chapter

has been used as basic text in statistics in Bureau-sponsored courses and seminars, including those for Electricity, Electronics, and Analytical Chemistry.

While concepts and techniques introduced in the original chapter remain

valid and appropriate, some additions on recent development of graphical

methods for the treatment of data would be useful. Graphical methods can be

used effectively to "explore" information in data sets prior to the application of classical statistical procedures. For this reason additional sections on statisti- cal graphics are added as a postscript.

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

graphics; measurement; metrology; plots; statistics; uncertainty.

13. AVAILABILITY

^

Unlimited

I I For Official Distribution. Do Not Release to NTIS

](^X](P^der From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C.

20402.

Order From National Technical Information Service (NTIS), Springfield, VA. 22161

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PRINTED PAGES

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15. Price

*U.S. GovernmenT Printing Office : 19SS - 2 U 1 - 5 9 7 / 9 2 5 S

1

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