Abstract:
A statistical software package for manufacturing and business applications is disclosed. The software package comprises an improved user interface that supports data collection, entry, and analysis features for manufacturing and business applications. Data entry is simplified by prompting the user for no more data than is needed to perform calculations and analysis. The software completes complex data analysis, interprets the data in the background, and reports simple analysis results to the user about the data that has been entered. The software supports user conducted tests but decides which test or tests are appropriate test and which results are relevant for the user. In an example embodiment, the software package is implemented as a web-based application with a central database accessible through a web service to provide support to the user. In an example embodiment, the application provides data collection, data entry, analysis, interpretation, graphing, and reporting functionality.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     None. 
     BACKGROUND OF THE INVENTION 
     Many business management strategies strive to improve the quality of process outputs by identifying and removing the causes of defects or errors and minimizing variability in the manufacturing and business processes. The strategies often use a set of quality management methods, including statistical methods, and suggest creating a special infrastructure of people within the organization (e.g., “Black Belts,” “Green Belts,” etc.) who are experts in these methods. Projects in the organization may be carried out according to a method and follow a defined sequence of steps toward a specified goal (e.g., a quantified manufacturing tolerance). 
     Although application of statistical methods to manufacturing and business processes is known to be an effective way to achieve improvements, learning and developing appropriate skills in the application of statistical methods requires patience and diligence. Because manual and human-based decisions are the centerpiece of today&#39;s art, proficiency in application of statistical methods for manufacturing and business requires not only an understanding of statistical concepts, methodologies, and calculations but mastery of the statistical analysis software packages that are used for calculation of statistics. For many individuals, mastery of statistics fundamentals is further complicated by the required mastery of the software packages. One of the reasons statistical software packages are difficult even for organizational experts to master is that the interfaces are cumbersome and non-intuitive. They often rely on hundreds of hierarchical menus and submenus that a user must review to set appropriate parameters or to select the appropriate tool. Some packages provide wizards for a few, but not all, of the available tools. Because no wizards are provided for important tools such as nonparametric hypothesis testing and nonlinear regression tests, existing packages are not very useful for manufacturing and business applications. While many statistical software packages are feature-rich and very powerful, they provide far more functionality than is needed for many manufacturing and business applications and therefore, are very difficult to use. In addition, for many unskilled users in statistics, they do not support automated statistical methods that are important for manufacturing and business process statistical analysis. There is a need for a statistical software package directed to mainstream, unskilled users in statistics for manufacturing and business applications with an intuitive, automated and improved user interface. There is a need for a statistical software package with an interface that allows users unskilled in the art of statistics to produce high quality statistical and data analysis with minimal user input. 
     SUMMARY OF THE INVENTION 
     The present disclosure is directed to a statistical software package for manufacturing and business applications. The software package comprises an improved user interface that supports data collection, entry, and analysis features for manufacturing and business applications. Data entry is simplified by prompting the user for no more data than is needed to perform calculations and analysis. The software completes complex data analysis, interprets the data in the background, and reports simple analysis results to the user about the data that has been entered. The software supports user conducted tests but decides which test or tests are appropriate and which results are relevant for the user. In an example embodiment, the software package is implemented as a web-based application with a central database accessible through a web service to provide support to the user. 
     In an example embodiment, the application provides the following functionality: 
     
       
         
               
             
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Application Functionality 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Data Collection: 
                 Prepares data collection sheet for the user to collect data  
               
               
                   
                 for various tests and analysis with correct sample sizes  
               
               
                   
                 and power of test 
               
               
                 Data Entry: 
                 Spreadsheet format that allows data to be imported or  
               
               
                   
                 inputted manually 
               
               
                 Analyses: 
                 uses raw data to check variables for stability and then  
               
               
                   
                 conducts various statistical analyses (including descrip- 
               
               
                   
                 tive statistics, process performance capability, differ- 
               
               
                   
                 ences and associations between variables or processes),  
               
               
                   
                 selects correct tests, tools and methods using system 
               
               
                   
                 criteria and algorithms to collect baseline statistics  
               
               
                   
                 and store in database 
               
               
                 Interpretation:  
                 Uses algorithms and system rules to interpret the  
               
               
                   
                 stored results 
               
               
                 Graphing: 
                 Graphs are generated automatically and accompany  
               
               
                   
                 analyses based on the tests that are executed 
               
               
                 Reporting: 
                 Analysis and interpretation are presented in a gener- 
               
               
                   
                 ated report; system presents an interactive display  
               
               
                   
                 of the analysis with graphics to describe the data/process 
               
               
                 Evaluating: 
                 User may evaluate the process at suggested values 
               
               
                   
               
             
          
         
       
     
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow diagram of a data analytics system according to an example embodiment; 
         FIG. 2  is a high-level work flow diagram according to an example embodiment; 
         FIG. 3  is an illustration for an example pipe diameter analysis according to an example embodiment; 
         FIG. 4  is a sample login in screen according to an example embodiment; 
         FIG. 5  is a sample data entry screen according to an example embodiment; 
         FIGS. 6A-6B  are sample data analysis screens according to an example embodiment; 
         FIG. 7  is a measured data analyze algorithm according to an example embodiment; 
         FIGS. 8A-8C  are sample graphs according to an example embodiment; 
         FIG. 9  is a sample summary report according to an example embodiment; 
         FIGS. 10A-10C  are sample data collection elements for single and multiple variable analysis according to an example embodiment; 
         FIG. 11  lists variable definitions according to an example embodiment; and 
         FIG. 12  is a system architecture diagram of a software application according to an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Referring to  FIG. 1 , a flow diagram of a data analytics system according to an example embodiment is shown. In an example embodiment, the system comprises four primary algorithms: analyze algorithm  100 ; compare algorithm (for both differences and associations between variables)  102 ; relate algorithm (for both observed and controlled experiments)  104 ; and valid model algorithm  106 . The algorithms represent the most common statistical analyses used for quality improvement and data analysis. The analyses apply to both individual and multiple variable studies. In an example embodiment, a web-based software application may be used to analyze products and services outputs across numerous industries. Based on minimal user input, the system continually pares down the tool choices and eliminates tools that are not needed to move from general-to-specific tool selections for particular process data. The tool choices and data checks are made continuously without user involvement for each algorithm. Prior art methods typically require an experienced practitioner to perform a step-by-step analysis in which the results are verified and interpreted one at a time. 
     The analyze algorithm  100  evaluates whether a process is capable of producing products or services that meets customer or other requirements. In an example embodiment, the analyze algorithm includes three mainstream statistical analysis methods to examine a single variable process data. These methods are: 
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 Analyze Algorithm Statistical Analysis Methods 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Basic statistics 
                 Evaluates process average, variation and  
               
               
                   
                 108 
                 shapes of data 
               
               
                   
                 Statistical Process  
                 Evaluates whether a process is stable 
               
               
                   
                 Control 110 
                 and consistent (no special issues  
               
               
                   
                   
                 present in the process) 
               
               
                   
                 Capability Analysis 
                 Evaluates capability based on a single  
               
               
                   
                 112 
                 process variable to assess the percent  
               
               
                   
                   
                 defective rate of the process (the percentage  
               
               
                   
                   
                 of products or services that fail to meet 
               
               
                   
                   
                 customer or other specifications) 
               
               
                   
                   
               
             
          
         
       
     
     To accurately estimate capability analysis of a process, the system examines conditions that can affect the validity of the results. To accomplish this, the system automatically performs several data checks as follows: (1) Does the data come from a stable process (e.g., does the data have any points outside of the control limits and is there a mean shift in the data)?; (2) Because the analysis estimates the capacity statistics based on normal distribution, does the process data follow a normal distribution (bell-shaped data with no outliers)?; and (3) Is the sample size large enough and is there enough data to accurately estimate the statistics? In an example embodiment, the software package automatically performs the following tests on the process data and presents these results in a summary report: 1) Does the data come from a normal distribution?; 2) Is the process stable and consistent?; and 3) Is the sample size large enough to estimate the statistics accurately? 
     Referring to  FIG. 2 , a high-level work flow diagram according to an example embodiment is shown. The high-level process comprises the following steps. First, the system utilizes the input from the user  200  to select the appropriate algorithm from the four main algorithms  202 . Second, the selected algorithm utilizes limited user input and built-in decision criteria to select the appropriate statistical analysis procedures to evaluate process data  204 . The analysis is conducted in the background and not visible to the user. Third, the selected algorithm utilizes the statistical output from the analysis and compares it with the built-in decision criteria to establish next steps in the flow  206 . The system continues the approach until it completes all necessary phases of the algorithm. Fourth, the system summarizes the findings and results in an easy-to-understand report using high quality graphics  208 . 
     Referring to  FIG. 3 , an illustration for an example pipe diameter analysis according to an example embodiment is shown. In the example, the analyze algorithm is applied to measured data (meaning an instrument was used to collect the data). For this example, data was collected and measured at the same time using a calibrated set of calipers. Two-inch metal pipes are manufactured with an acceptable customer specification of ±0.1 inches. A concern is identified regarding the consistency of the diameters to meet customer specifications and the high scrap rate in the process. The goal is to determine how many pipes are out of specification and being scrapped. Using the calipers, 10 finished pieces of pipes are measured from the process and the readings are documented to conduct the analysis. In this example, the sample size is small to simplify the explanation. 
     Referring to  FIG. 4 , a sample login screen according to an example embodiment is shown. A user access a web site where the software application executes and enters login data  400 . Referring to  FIG. 5 , a sample data entry screen according to an example embodiment is shown. In an example embodiment, the software supports data entry in a spreadsheet format that allows data to be imported or entered manually. In the pipe diameter example, the user enters pipe diameter data in the grid  500  by uploading the data from a file or manually keying in the data. The user then highlights the column(s) of interest for analysis. In this example, one column is shown for the pipe diameter  502 . The system repeats the analysis if two columns or more are selected. 
     Referring to  FIGS. 6A-6B , sample data analysis screens according to an example embodiment are shown. Referring to  FIG. 6A , the user selects the “analyze data” option  600  which causes an input dialogue box to appear as shown in  FIG. 6B  (the dialogue box can also be affixed to the side of the screen). In the dialogue box, the user is prompted for input to define the variables and process data for the software. In an example embodiment, the user is prompted to answer a few multiple-choice questions from a drop-down menu or to input numeric data. For the multiple-choice questions, the user may select one of two options depending on process data. These questions assist the software in paring down the analysis tools, methods, and choices and in eliminating analyses that are not applicable. The software continually pares down choices until it reaches the appropriate algorithm, tests, and analyses. For the pipe diameter example, user inputs (answers to the prompts) are shown in Table 3.  FIG. 11  provides a list of user input questions to support other algorithms. 
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Expanded View of User Input Questions 
               
             
          
           
               
                   
                   
                 OR User 
                   
               
               
                 User Input 
                 User Input 
                 Input  
                   
               
               
                 Questions 
                 Answer 1 
                 Answer 2 
                 Explanation 
               
               
                   
               
               
                 Is this a single or 
                 Single 
                 Multiple 
                 If user selects “single,” the system eliminates 
               
               
                 multiple variable 
                   
                   
                 multiple analysis and focuses on single analysis to 
               
               
                 analysis? 
                   
                   
                 conduct basics statistics, statistical process control, 
               
               
                   
                   
                   
                 and capability analysis. If multiple, it adds 
               
               
                   
                   
                   
                 regression and hypothesis testing. 
               
               
                 Was the data 
                 Measured 
                 Counted 
                 If user selects “measured,” the system ignores 
               
               
                 measured with 
                   
                   
                 discrete algorithm analysis and activates the 
               
               
                 an instrument or 
                   
                   
                 measured data algorithm for the analysis. If user 
               
               
                 was it counted? 
                   
                   
                 selects “counted,” the discrete data algorithm is 
               
               
                   
                   
                   
                 activated for the analysis. 
               
               
                 Were all 
                 All data points 
                 1-7 
                 If user selects “all collected at the same time,” this 
               
               
                 samples 
                 collected at 
                 OR 
                 activates the individual and moving range (I-MR) 
               
               
                 collected at the 
                 the same time 
                 ≧8 pieces 
                 statistical process control chart and analysis. 
               
               
                 same time or 
                   
                 collected at 
                 Between 1-7 activates the Xbar-Range chart and 
               
               
                 how many data 
                   
                 the same 
                 ≧8 actives the Xbar-Standard deviation chart 
               
               
                 points were 
                   
                 time 
                   
               
               
                 collected each 
                   
                   
                   
               
               
                 time (subgroup 
                   
                   
                   
               
               
                 size)? 
                   
                   
                   
               
               
                 Subgroup size is 
                   
                   
                   
               
               
                 the number of 
                   
                   
                   
               
               
                 data points 
                   
                   
                   
               
               
                 measured at the 
                   
                   
                   
               
               
                 same time. 
                   
                   
                   
               
               
                 What are the 
                 ±0.1 
                 None 
                 If user inputs a value, this response activates the 
               
               
                 acceptable 
                   
                   
                 measured capability algorithm to conduct the 
               
               
                 customer 
                   
                   
                 capability analysis. If user does not provide a 
               
               
                 specifications for 
                   
                   
                 value, the system eliminates capability analysis and 
               
               
                 this product or 
                   
                   
                 presents basic statistics and process statistical 
               
               
                 service 
                   
                   
                 control analysis only. 
               
               
                 (tolerance)? 
               
               
                   
               
             
          
         
       
     
     The software next activates the measured data analyze algorithm based on the user inputs above. Referring to  FIG. 7 , a measured data analyze algorithm according to an example embodiment is shown. The software analyzes a single continuous variable  700  by initially conducting a normality analysis  702  on the data in the background and comparing the test statistic—in this case the p-value (the test for pass or fail)  704 —to the built-in system criteria for the same statistic (p-value=0.05). This test determines whether there is a need to conduct a normal or non-normal capability analysis. Normal data follows a bell shaped curve and is not skewed in either direction. Data is normal if the test criteria p-value &gt;0.05  704  and data is non-normal if the p-value &lt;0.05  706 . In that case, non-normal capability analysis is used. 
     Referring to  FIG. 8A , a sample graph of normality test results is shown. The graph shows the results after the software executes statistical analysis for a normality test and generates the criteria for algorithm to compare to its built-in decision criteria. In this case, p-value for pipe diameter=0.8 which is &gt;0.05 indicating the data comes from a normal distribution and system can use normal capability analysis. 
     Referring again to  FIG. 7 , the software follows different branches  710  depending on the subgroup size  708 . In the example, the subgroup size is one because all data was collected at the same time under similar conditions. The software conducts a stability test  712  to make sure the process is stable (i.e., does not vary greatly) and it does not have unusual causes for variation. To do that, the software conducts two tests. In a first test, the software identifies points outside of the control limits (i.e., points &gt;3 standard deviations from the centerline). The test fails if one point falls outside the specified limits. The second test helps detect common out-of-control situations and improves the ability of the chart to detect small shifts in the mean. The second test also increases sensitivity enough to warrant a slight increase in the false alarm rate. The test fails when nine or more points are on one side of the centerline (shift in the mean). If either of these tests fails, the software indicates the failure (e.g., by show a red point marked with either number 1 or 2 signifying the failed test)  726 . In the pipe diameter example, the process is stable. If the tests fail, the user needs to use caution when interpreting the capability analysis because the process is out-of-control and needs to be corrected  728 . 
     Referring to  FIG. 8B , a sample graph of process control test results is shown. The process control test output is displayed after the software executes the statistical analysis for the process control test and generates the criteria for the algorithm to compare to its built in decision criteria. In this example, no points fell outside the control limits, indicating the process is in control and the system can proceed with capability analysis without restrictions or alarms. 
     Referring again to  FIG. 7 , because the process is normal and stable  714 , the software proceeds to calculate the percent defective rate in the process  716  (i.e., number of units out of specification). In an example embodiment, if the data is normal and stable, the software executes a normal capability analysis. Otherwise, the software executes a different analysis such as the Weibull for non-normal capability analysis. 
     Referring to  FIG. 8C , a sample capability analysis graph with normal data is shown. The normal capability analysis output is displayed after the software executes the statistical capability analysis and generates the criteria for the algorithm to compare to its built-in decision criteria. In this case, z-score for pipe diameter=−0.53 which is &lt;3 indicating the data produces large number of defects (7/10 units are out of spec). The conclusion is that the manufacturing process needs to be improved. 
     Referring again to  FIG. 7 , the software next computes the key capability indices such as Pp/Cp (process consistency), Ppk/Cpk (process precision, meeting a goal or a target), PPM (parts per million), average, mean, standard deviation (measures variation in the process), interquartile range (measures variation for non-normal data), z-score (process capability metric that depends on PPM value), and defective rate percentage (amount of pipe outside the spec limit)  718 . If the process z-score ≧3, the process may be improved slightly  720 . If the process z-score is &lt;3, the process needs substantial improvement  722 . The system stores these metrics in the database for later use  724 . The software further presents a summary of the analysis in the reports. 
     Referring to  FIG. 9 , a sample summary report according to an example embodiment is shown. The report comprises a summary of the analysis and graphs. The user may customize the reports or use the hyperlinks as needed to view additional details of the analysis. 
       FIG. 10A , a sample data collection elements for a single variable analysis according to an example embodiment is shown.  FIG. 10B , shows similar elements for multiple variable analysis according to an example embodiment. The multiple variable analysis data collection elements support regression and hypothesis testing. Referring to  FIG. 10B , the multiple variable data collection sheet comprises an editable column for each X variable and each Y variable. Sample size recommendations are built into the test and methods and system will alert users any time the sample sizes are inadequate. 
     For data collection with subgroups, the recommended subgroup size is eight data points (meaning eight samples per subgroup collected under similar conditions) which increases the precision of the analysis, and the subgroup options are as shown in  FIG. 10C . As with single variable analysis, after the data is entered in the spreadsheet, dialogue boxes guide the user to define the variables. 
     User input questions and variable definitions according to an example embodiment are shown in Table 4. User input questions appear based on the selected test. When information about variables is entered or collected from analysis, the system retains that information even if the column moves. Hyper-dynamic text is used to define these terms as the mouse or other input device hovers over them. 
     
       
         
               
             
               
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 User Input Questions 
               
             
          
           
               
                   
                 Question for User Input 
                   
                   
                   
               
               
                 Question 
                 for the Algorithms to 
                   
                 Default/Auto 
                   
               
               
                 # 
                 Execute 
                 Algorithm 
                 Detect/User input 
                 User Input 
               
               
                   
               
               
                 Q1 
                 Individual (s) vs. Multi-  
                 Analyze 
                 Individual 
                 Multiple-variable 
               
               
                   
                 variable analysis? 
                   
                 (Analyze - basic 
                 (Compare/relate) 
               
               
                   
                   
                   
                 statistics, Statistical 
                   
               
               
                   
                   
                   
                 Process Control, 
                   
               
               
                   
                   
                   
                 Capability) 
                   
               
               
                 Q2 
                 Type of Data? 
                 Analyze 
                 Continuous data 
                 (% Defective or 
               
               
                   
                   
                   
                 (measured) 
                 Defects per Unit, 2- 
               
               
                   
                   
                   
                   
                 way tables) 
               
               
                   
                   
                   
                   
                 (Counted) 
               
               
                 Q3 
                 Can outliers be removed? 
                 Analyze, 
                 Yes: Outlier are 
                 No: Outlier are 
               
               
                   
                   
                 Relate 
                 explainable 
                 natural to the process 
               
               
                 Q4 
                 Subgroup size? 
                 Analyze 
                 1, or &lt;8 or ≧8 
                   
               
               
                   
                   
                   
                 (Analyze algorithm and 
                   
               
               
                   
                   
                   
                 SPC charts) 
                   
               
               
                 Q5 
                 What are the Spec limits? 
                 Analyze 
                   
                 USL and or LSL 
               
               
                   
                   
                 (upr, lwr, or both) 
                   
                 (capability analysis, 
               
               
                   
                   
                   
                   
                 SPC) 
               
               
                 Q6 
                 Number of independent 
                 Compare 
                 User input 
                 To establish 2-way 
               
               
                   
                 variables? 
                   
                   
                 ANOVA, or 2-way 
               
               
                   
                   
                   
                   
                 tables 
               
               
                 Q7 
                 Number of Groups? 
                 Compare 
                 1 
                 2, 3+ 
               
               
                   
                   
                   
                 (compare, chi-sqr) 
                 (compare, chi-sqr 
               
               
                 Q8 
                 What Target/Acceptance 
                 Compare 
                 User input 
                 User input 
               
               
                   
                 value for test? This is 
                   
                 (compare, capability) 
                 (compare, capability) 
               
               
                   
                 optional at times. 
                   
                   
                   
               
               
                 Q9 
                 Which column (C1, C2) 
                 Compare 
                 User selection 
                   
               
               
                   
                 contains the Defects or 
                   
                 (discrete analysis for 
                   
               
               
                   
                 defectives 
                   
                 compare, capability) 
                   
               
               
                 Q10 
                 Which column contains 
                 Compare 
                 User selection 
                   
               
               
                   
                 Total units tested 
                   
                 (this this auto selected 
                   
               
               
                   
                   
                   
                 once user selects (C1 or 
                   
               
               
                   
                   
                   
                 C2 from item above) 
                   
               
               
                 Q11 
                 Are the variables 
                 Compare 
                 Independent 
                 Dependence 
               
               
                   
                 Dependent (correlated)  
                   
                 (compare) 
                 compare as in pair-t, 
               
               
                   
                 or Independence? 
                   
                   
                 or relate as in Y = f(x)) 
               
               
                 Q12 
                 Sample with replacement 
                 Analyze % 
                 Without replacement 
                 Without replacement 
               
               
                   
                   
                 defective 
                   
                   
               
               
                 Q13 
                 When, multiple columns 
                 Relate 
                 Note: once user selects  
                 User input 
               
               
                   
                 are selected, which 
                   
                 the column, the systems  
                 (Relate, 2-way 
               
               
                   
                 column is the dependent 
                   
                 assumes the other 
                 tables, 2-way 
               
               
                   
                 response variable Y? 
                   
                 variables are 
                 ANOVA) 
               
               
                   
                   
                   
                 independent X(s) 
                   
               
               
                 Q14 
                 Column selected are 2- 
                 Chi-Sqr, 
                 Yes 
                 No 
               
               
                   
                 way Table? 
                 Compare 
                 (Compare chi-square) 
                 (compare) 
               
               
                 Q15 
                 Alpha level 
                 All 
                 α = 0.05, with 95% C.I.  
                 User could change 
               
               
                   
                 Significance of the test 
                   
                   
                 default alpha based 
               
               
                   
                   
                   
                   
                 on industry 
               
               
                   
               
             
          
         
       
     
     Referring to  FIG. 11 , the variables definition dialogue boxes provide input to the system which allows it to select the appropriate algorithm for the analysis. Data collection sheets are generated for the appropriate process and the user provides input as appropriate to execute regression or hypothesis testing analysis. For example, to compare groups, the user is prompted for the number of groups to be compared. As with the single variable analysis, the analyze phase begins when the user selects the analyze option and the software proceeds with little interaction from the user. 
     Referring again to  FIG. 10A , after completing the variable definition and entering the variable specifications, the user may select any column(s) of the data collection sheet for analysis and then select the analyze option. In the case of comparison or relationship (hypothesis and regression), after the user selects the columns for analysis, he is prompted to select the hypothesis (compare) or regression (relate) option. 
     The flow diagram in  FIG. 7  is a representative example of an analyze algorithm for a single variable according to an example embodiment. Similar methods and algorithm workflows are used for multiple variable evaluations. As indicated in  FIG. 7 , a user may be prompted to provide input at various points based on the user input questions outlined in Table 4 and  FIG. 11 . Otherwise the software proceeds with the analysis based on the built in decision criteria and workflow. 
     Referring to  FIG. 12 , a system architecture diagram of a software application according to an example embodiment is shown. As indicated in  FIG. 12 , the software application may comprise a presentation layer  1200 , business logic  1202 , statistical services  1204 , and business entities  1206 . The presentation layer  1200  and business logic layer  1202  may be implemented using Microsoft® Windows® Presentation Foundation development tools. 
     Statistical services  1204  may be provided using integrated software components as indicated in Table 5. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 Statistical Components 
               
             
          
           
               
                 Component 
                 Services 
               
               
                   
               
               
                 Regression 
                 Basic Statistical information about a series 
               
               
                   
                 Regression analysis (linear, nonlinear, single and  
               
               
                   
                 multivariate) 
               
               
                 Statistical 
                 SPC (Variable, Attribute, and SPC tests) 
               
               
                 Process 
                 Capability Analysis (linear, nonlinear) 
               
               
                 Control 
                   
               
               
                 Hypothesis 
                 Hypothesis Testing (1 Sample, 2 Sample, Parametric,  
               
               
                 Testing 
                 Nonparametric, ANOVA &gt;3, Chi Squared) 
               
               
                 Design of 
                 DOE (linear, nonlinear) 
               
               
                 Experiments 
               
               
                   
               
             
          
         
       
     
     The interface of the disclosed statistical analysis system and method allows many users unskilled in the art of statistics to produce high quality statistical and data analysis with minimal user input. While certain embodiments of the disclosed statistical analysis software system and method are described in detail above, the scope of the invention is not to be considered limited by such disclosure, and modifications are possible without departing from the spirit of the invention as evidenced by the claims. For example, elements of the user interface and analysis reporting may be varied and fall within the scope of the claimed invention. Various aspects of data entry and analysis may be varied and fall within the scope of the claimed invention such as design of experiments, gage studies, logistic regression and the like. One skilled in the art would recognize that such modifications are possible without departing from the scope of the claimed invention.