What’s New in SAS/QC 12.1

Overview

SAS/QC 12.1 includes three new procedures for multivariate process monitoring, as well as enhancements to the CAPABILITY, PARETO, and RELIABILITY procedures.
In previous years, SAS/QC® software was updated only with new releases of Base SAS® software, but this is no longer the case. This means that SAS/QC software can be released to customers when enhancements are ready, and the goal is to update SAS/QC every 12 to 18 months. To mark this newfound independence, the release numbering scheme for SAS/QC is changing with this release. This new numbering scheme will be maintained when new versions of Base SAS and SAS/QC ship at the same time. For example, when Base SAS 9.4 is released, SAS/QC 13.1 will be released.

Production Status for the MVP Procedures

The MVPMODEL, MVPMONITOR, and MVPDIAGNOSE procedures, referred to collectively as the MVP procedures, are used together for multivariate process monitoring. The MVPMODEL and MVPMONITOR procedures were introduced as experimental procedures in SAS/QC 9.3 and are production for SAS/QC 12.1. The MVPDIAGNOSE procedure is new in SAS/QC 12.1.
The MVPMODEL procedure provides computational and graphical tools for building a principal component model from multivariate process data in which the measured variables are continuous and correlated. It implements principal component analysis (PCA) techniques that evolved in the field of chemometrics for monitoring hundreds or even thousands of correlated process variables; see Kourti and MacGregor (1995, 1996) for an introduction. A principal component model reduces the dimensionality of the data by projecting the process measurements to a low-dimensional subspace that is defined by a small number of principal components. This subspace is known as the model hyperplane.
The principal component model and other output from PROC MVPMODEL serve as input to the MVPMONITOR and MVPDIAGNOSE procedures.
The MVPMONITOR procedure creates control charts for multivariate process data by using the principal component model that is produced by the MVPMODEL procedure. Multivariate control charts detect unusual variation that would not be uncovered by individually monitoring the process variables with univariate control charts, such as Shewhart charts. PROC MVPMONITOR creates two types of multivariate control chart. Inline Graphic of: $T^2$ charts detect unusual variation within the model hyperplane, whereas squared prediction error (SPE) charts detect unusual variation from the hyperplane.
The MVPDIAGNOSE procedure produces principal component score plots and process variable contribution plots that are used to investigate the causes of unusual variation in a process.

CAPABILITY Procedure Enhancements

The CAPABILITY procedure supports several new options. The CDFPLOT, COMPHISTOGRAM, HISTOGRAM, PPPLOT, PROBPLOT, and QQPLOT statements support the following new options for specifying titles and footnotes in graphs that are produced by using ODS Graphics:
  • ODSFOOTNOTE= adds a footnote to the graph.
  • ODSFOOTNOTE2= adds a secondary footnote to the graph.
  • ODSTITLE= specifies the graph title.
  • ODSTITLE2= specifies a secondary graph title.
You can use these options to specify your own graph titles and footnotes without modifying ODS graph templates or using the ODS Graphics Editor.
The CDFPLOT, COMPHISTOGRAM, HISTOGRAM, PROBPLOT, and QQPLOT statements support the following new options for displaying reference lines at the values of computed statistics:
  • STATREF= specifies keywords that identify the statistics.
  • CSTATREF= specifies the colors of the reference lines.
  • LSTATREF= specifies the line types of the reference lines.
  • STATREFLABELS= specifies labels for the reference lines.
  • STATREFSUBCHAR= specifies a substitution character for incorporating statistic values into reference line labels.
For example, specifying STATREF=MEAN in a HISTOGRAM statement produces a histogram that has a vertical reference line at the mean of the data.
The COMPHISTOGRAM and HISTOGRAM statements support the new CLIPCURVES option, which clips fitted distribution curves that extend above the highest histogram bar. This eliminates compression of the histogram bars caused by extremely high fitted curve peaks.
The OUTPUT statement supports the following new options:
  • CIPCTLDF= computes distribution-free confidence limits for percentiles that you request by specifying the PCTLPTS= option.
  • CIPCTLNORMAL= computes confidence limits that assume normality for percentiles that you request by specifying the PCTLPTS= option.
  • PCTLGROUP= controls how variables that you request by specifying the PCTLPTS= option are grouped in the OUTPUT data set.
In addition, the CHREF=, CVREF=, LHREF=, and LVREF= options have been enhanced. These options now accept lists of values so that different reference lines in a single graph can be displayed by using different colors and line types. They are available in the CDFPLOT, COMPHISTOGRAM, HISTOGRAM, PPPLOT, PROBPLOT, and QQPLOT statements.

PARETO Procedure Enhancements

The HBAR and VBAR statements now support the CHARTTYPE= option, which enables you to create variations on the traditional Pareto chart, as described by Wilkinson (2006). These statements also support the following new options for specifying titles and footnotes in graphs that are produced by using ODS Graphics:
  • ODSFOOTNOTE= adds a footnote to the graph.
  • ODSFOOTNOTE2= adds a secondary footnote to the graph.
  • ODSTITLE= specifies the graph title.
  • ODSTITLE2= specifies a secondary graph title.
These options enable you to specify your own graph titles without modifying ODS graph templates or using the ODS Graphics Editor.

RELIABILITY Procedure Enhancements

The RELIABILITY procedure now supports the following statements that provide further analysis of regression models that are fit by using a MODEL statement:
  • The EFFECTPLOT statement produces a display of the fitted model and provides options for changing and enhancing the displays.
  • The ESTIMATE statement provides a mechanism for obtaining custom hypothesis tests.
  • The LSMEANS statement computes and compares least squares means of fixed effects.
  • The LSMESTIMATE statement provides a mechanism for obtaining custom hypothesis tests among least squares means.
  • The SLICE statement provides a general mechanism for performing a partitioned analysis of the least squares means for an interaction.
  • The STORE statement saves the context and results of the statistical analysis to an item store.
  • The TEST statement performs F tests for model effects that test Type I, Type II, or Type III hypotheses.
These statements are common to many SAS/STAT® procedures and are fully documented in the SAS/STAT 12.1 User’s Guide.
PROC RELIABILITY also provides the following new features:
  • confidence bands for mean and intensity functions for recurrent events
  • two-sample log-rank-type test for recurrent events
  • trend tests for recurrent events parametric models
  • Duane plots
  • profile likelihood confidence intervals for survival function

References

  • Kourti, T. and MacGregor, J. F. (1995), “Process Analysis, Monitoring and Diagnosis, Using Multivariate Projection Methods,” Chemometrics and Intelligent Laboratory Systems, 28, 3–21.
  • Kourti, T. and MacGregor, J. F. (1996), “Multivariate SPC Methods for Process and Product Monitoring,” Journal of Quality Technology, 28, 409–428.
  • Wilkinson, L. (2006), “Revising the Pareto Chart,” The American Statistician, 60, 332–334.
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