Process Analysis Procedures Startup Panel and Quick Tab

Ribbon bar. Select the Statistics tab. In the Industrial Statistics group, click Process Analysis to display the Process Analysis Procedures Startup Panel.

The Startup Panel contains one tab: Quick.

See also the Process Analysis Overviews and Examples.

Process capability analysis & tolerance intervals, raw data

Select Process capability analysis & tolerance intervals, raw data.
Click the OK button to display the Process Capability Analysis Setup-Raw Data dialog box.
You also can double click on this option to display the dialog box.
Use the options in this dialog box to perform a complete process (machine) capability analysis. A raw data file is required as input for this method. This procedure and its applications are described in the Basic Overviews.
Note: raw data. In this procedure, Statistica expects as input a raw data file, with an optional grouping variable to indicate to which one of several samples each observation belongs.

Process capability analysis & tolerance intervals, aggreg. data

Select Process capability analysis & tolerance intervals, aggreg. data.
Click the OK button to display the Process Capability Analysis Setup-Aggregated Data dialog box.
Use the options in this dialog box to perform a complete process (machine) capability analysis. A data file containing aggregated results is required as input for this method. This procedure and its applications are described in the Introductory Overview.
Note: Note: Aggregated data. In this setting, Statistica assumes that the input data file contains already aggregated results, that is, that each row in the input data file contains the mean, range (standard deviation, variance), and samples size for one sample. This type of input is also allowed in the Quality Control module for computing quality control charts for variables. If you have aggregated data, then no distributions can be fitted to the data.

Process capability ISO/DIN (Time dependent distribution model)

Select Process capability ISO/DIN (Time dependent distribution model).
Click the OK button to display the ISO 2147/DIN 55319 Process Capability Setup dialog box - Quick tab.

Capability ratios for True Position

Select Capability ratios for True Position.
Click the OK button to display the Capability Ratios for True Position dialog box - Specification tab.

Gage repeatability & reproducibility

Select Gage repeatability & reproducibility.
Click the OK button to display the Repeatability & Reproducibility Analysis dialog box - Generate design
tab
.
Use the options in this dialog box to design and analyze gage repeatability and reproducibility studies. When analyzing the results of a study, a data file (either a standard Statistica data file or a standard R & R data sheet) is required. This procedure and its applications are described in the Introductory Overviews (see also Methods for Analysis of Variance for related procedures).

Gage capability

Select Gage capability.
Click the OK button to display the Gage capability dialog box.
Use the options in this dialog box to generate a design for a Measurement Capability Study.

Gage linearity

Select Gage linearity.
Click the OK button to display the Gage Linearity dialog box. With Statistica Gage Linearity, you can determine how much of the variation in your process is actually coming from the measurement system. The determination of the sources of variation in measurement systems is particularly important in Quality Control problems, where measurements are often made with finite precision due to limited accuracy of the gage, or because of existing environmental factors that are adding further inaccuracies to gage readouts.

Attribute gage study (Analytic method)

Select Attribute gage study (Analytic method).
Click OK to display the Attribute gage study (Analytic method) dialog box.
Use the options in this dialog box to assess the amount of repeatability and bias in an attribute measurement study. The analysis can be performed using either a single or double limit system. Both the AIAG method and standard regression method for testing bias = 0 can be used. This procedure and its applications are described in the Introductory Overview and Computational Details.

Attribute agreement analysis

Select Attribute agreement analysis.
Click OK to display the Attribute agreement analysis dialog box.

MSA Attribute Data

Select MSA Attribute Data.
Click OK to display the MSA Attribute data dialog box.

Capability analysis - Binomial

Select Capability analysis - Binomial.
OK to display the
Capability analysis (Binomial
) dialog box.
Use the options in this dialog box to fit a binomial distribution to data representing binomial counts or proportions (e.g., parts defective in samples of parts). Statistica will fit a binomial distribution to the data, compute various graphical and Statistical summaries, and estimate the expected PPM (Parts per Million) defective based on the fitted distribution, and user-defined upper specification limits (on the acceptable number of defectives). A summary process capability index is also computed.

Capability analysis - Poisson

Select Capability analysis - Poisson.
Click OK to display the
Capability analysis (Poisson distribution
) dialog box.
Use the options in this dialog box to fit a Poisson distribution to data representing counts or rates of defects (or some other event) per unit ( number of defects per silicon wafer). Statistica will fit a Poisson distribution to the data, compute various graphical and Statistical summaries, and estimate the expected PPM (Parts per Million) defective based on the fitted distribution, and user-defined upper specification limits (on the acceptable number of defects per unit). A summary process capability index is also computed.

Weibull analysis & reliability/failure time analysis

Select Weibull analysis & reliability/failure time analysis.
Click the OK button to display the
Weibull & Reliability/Failure Time Analysis-Raw Data
dialog box.
Use the options in this dialog box to fit the two- or three-parameter Weibull distribution to complete or censored data sets. Both single and multiple censoring is supported, and raw data as well as aggregated (grouped) data can be analyzed. Statistica computes maximum likelihood parameter estimates and their standard errors, and provides numerous options for computing the reliability function based on those estimates as well as user-defined parameter values; nonparametric (distribution-free) methods are also provided. These procedures and their applications are described in the Introductory Overview. Similar procedures for analyzing censored data sets are also provided in the Survival Analysis module.

Make Weibull paper

Select Make Weibull paper.
Click the OK button to display the Weibull Probability Paper dialog box.
Use the options in this dialog box to produce a customized Weibull probability plot; when no data is plotted but only the probability scale and the logarithmic scale for failure times is shown, such plots are also called probability paper. The default location (offset) parameter is equal to 0 (zero); however, if you have previously fitted the Weibull distribution to a variable (see the Reviewing Results for Raw Data dialog box), the respective location parameter value is remembered.

Sampling plans for means, proportions, & Poisson frequencies

Select Sampling plans for means, proportions, & Poisson frequencies.
Click the OK button to display the
Sampling Plans
dialog box.
Use the options in this dialog box to produce fixed and sequential sampling plans for normal means, binomial proportions, or Poisson frequencies. The different sampling plans and their applications are described in the Introductory Overviews.

Cause-effect (Ishikawa, Fishbone) diagrams

Select Cause-effect diagrams.
Click the OK button to display the C
ause-and-Effect (Ishikawa) Diagrams
dialog box.
Use the options in this dialog box to create Cause-and-effect diagrams (also referred to as Fishbone charts, Ishikawa charts). The cause-and-effect diagram provides an efficient summary of factors that impact a process, and hence can be used as a map to guide the overall quality improvement efforts. The cause-and-effect diagram plays a central role in Six Sigma quality programs. During the first stage of the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, this diagram can be of great utility in order to identify the areas, departments, processes, and stakeholders that should be involved in the effort. See Harry and Schroeder (2000), Pyzdek (2001), or Rath and Strong (2000) for additional details; see also Six Sigma or Cause-and-Effect diagram in the Introductory Overview.

OK

Select the desired Process Analysis procedure on the Quick tab.
Click the OK button to display the corresponding analysis dialog box.

Cancel

Click the Cancel button to close the Startup Panel without performing an analysis.

Options

See Options Menu for descriptions of the commands on this menu.

Open Data

Click the Open Data button to display the Select Data Source dialog box, which is used to choose the spreadsheet on which to perform the analysis. The Select Data Source dialog box contains a list of the spreadsheets that are currently active.

Select Cases

Click the Select Cases button to display the Analysis/Graph Case Selection Conditions dialog box, which is used to create conditions for which cases will be included (or excluded) in the current analysis. More information is available in the case selection conditions overview and syntax summary.

W

Click the W (Weight) button to display the Analysis/Graph Case Weights dialog box, which is used to adjust the contribution of individual cases to the outcome of the current analysis by weighting those cases in proportion to the values of a selected variable.

See also the Process Analysis Overviews and Examples.