SAS/QC^{®}
 The ANOM Procedure PDF 
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A graphical and statistical method for simultaneously comparing treatment means with their overall mean at a specified significance level. You can use the ANOM procedure to create ANOM charts for various types of response data, including continuous measurements, proportions, and rates.  The CAPABILITY Procedure PDF 
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A process capability analysis compares the distribution of output from an incontrol process to its specification limits to determine the consistency with which the specifications can be met..  The CUSUM Procedure PDF 
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Creates cumulative sum control charts, also known as cusum charts, which display cumulative sums of the deviations of measurements or subgroup means from a target value. Cusum charts are used to decide whether a process is in statistical control by detecting a shift in the process mean.  The FACTEX Procedure PDF 
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Constructs orthogonal factorial experimental designs. These designs can be either full or fractional factorial designs, and they can be with or without blocks. You can also construct designs for experiments with multiple stages, such as splitplot and splitlot designs. After you have constructed a design by using the FACTEX procedure and run the experiment, you can analyze the results with a variety of SAS procedures including the GLM and REG procedures.  The ISHIKAWA Procedure PDF 
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The Ishikawa diagram, also known as a causeandeffect diagram or fishbone diagram, is one of the seven basic tools for quality improvement in Japanese industry. It is used to display the factors that affect a particular quality characteristic or problem.  The MACONTROL Procedure PDF 
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Creates moving average control charts, which are tools for deciding whether a process is in a state of statistical control and for detecting shifts in a process average..  The MVPDIAGNOSE Procedure PDF 
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Used in conjunction with the MVPMODEL and MVPMONITOR procedures to monitor multivariate process variation over time in order to determine whether the process is stable or to detect and diagnose changes in a stable process.  The MVPMODEL Procedure PDF 
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Used in conjunction with the MVPMONITOR and MVPDIAGNOSE procedures to monitor multivariate process variation over time in order to determine whether the process is stable or to detect and diagnose changes in a stable process.  The MVPMONITOR Procedure PDF 
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Used in conjunction with the MVPMODEL and MVPDIAGNOSE procedures to monitor multivariate process variation over time in order to determine whether the process is stable or to detect and diagnose changes in a stable process.  The OPTEX Procedure PDF 
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Searches for optimal experimental designs. You specify a set of candidate design points and a linear model, and the procedure chooses points so that the terms in the model can be estimated as efficiently as possible.  The PARETO Procedure PDF 
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Creates Pareto charts, which display the relative frequency of qualityrelated problems in a process or operation. The frequencies are represented by bars that are ordered in decreasing magnitude. Thus, a Pareto chart can be used to decide which subset of problems should be solved first or which problem areas deserve the most attention.  The RAREEVENTS Procedure PDF 
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Produces control charts for rare events.  The RELIABILITY Procedure PDF 
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Provides tools for reliability and survival data analysis and for recurrent events data analysis.  The SHEWHART Procedure PDF 
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A graphical and analytical tool for deciding whether a process is in a state of statistical control. You can use the SHEWHART procedure to display many different types of control charts, including all commonly used charts for variables and attributes.
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SAS/QC Documentation Examples
For examples in the documentation, go to SAS/QC software documentation examples.SAS/QC Software Examples
The following SAS/QC software example is not included in the SAS/QC documentation and are available only on the Web.
PROC OPTEX
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2017 Papers

Telling the Story of Your Process with Graphical Enhancements of Control Charts
Ransdell, Bucky; SAS Institute, Inc. 2017This paper explains how you can use the SHEWHART procedure in SAS/QC software to make the following enhancements: display multiple sets of control limits that visualize the evolution of the process, visualize stratified variation, explore withinsubgroup variation with boxandwhisker plots, and add information that improves the interpretability of the chart.
SAS/QC software provides a comprehensive set of tools for statistical quality improvement and design of experiments. You can use these tools to organize quality improvement efforts, design and analyze experiments for process discovery and optimization, apply Taguchi methods for quality engineering, establish statistical control of a process, maintain statistical control and reduce variation, assess process capability, and analyze product reliability.
These methods were introduced in the manufacturing and process industries, and they continue to be used in modern industrial environments, where they are emphasized by Six Sigma programs. Statistical methods for quality improvement are also finding new applications in other sectors. For example:
 Banking call centers are applying statistical process control to callhandling times in order to increase customer satisfaction and value.
 Health care providers are using control charts and analysis of means to monitor utilization of expensive resources and procedures, such as CAT scans.
 Direct marketers are applying design of experiments to campaign planning and website design in order to improve customer response rates.
Common to all these situations is the concept of a process, together with the need to understand the types of variation that affect the process. Statistical process control provides the basis for analyzing and reducing this variability, so that the process becomes stable and predictable. Consequently, management can decide when to respond early to problems. Design of experiments provides the basis for understanding which factors influence a response, so that the process can be optimized.
Control Chart for Process that Displays Stable Variation