SAS/QC Software
Capabilities
Basic Quality Improvement
The PARETO procedure creates basic Pareto charts, one-way and two-way comparative Pareto charts for making comparisons across
levels of classification variables, weighted Pareto charts for incorporating costs. The ISHIKAWA procedure provides an interactive
graphics environment for creating and modifying Ishikawa diagrams with annotation and drill-down to sub-diagrams.
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Design of Experiments
The ADX Interface guides you through the steps of creating a design, recording responses, analyzing the data, and generating a
report. ADX provides two-level screening designs, response surface designs, optimal designs, mixture designs, mixed-level designs,
Taguchi designs, split-plot designs, and general factorial designs. Interactive graphics in ADX include main effects plots,
interaction plots, cube plots, scatter plots, factorial plots, Lenth plots, Bayes plots, prediction profile plots, contour plots,
and surface plots. Analysis methods include regression analysis, analysis of variance, residual analysis, outlier and influential
observation analysis, optimal Box-Cox transformations, canonical analysis, and a variety of methods for selecting active effects
in saturated two-level designs.
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Process Capability Analysis
The CAPABILITY procedure provides descriptive statistics, standard capability indices and confidence intervals (Cp, Cpk, CPL, CPU,
Cpm, k), specialized capability indices, confidence, tolerance and prediction intervals for normal parameters, parameter
estimation and goodness-of-fit tests for a variety of distributions (normal, lognormal, exponential, Weibull, gamma, beta, SU,
SB), and estimates for the probability of exceeding the specification limits. The CAPABILITY procedure also creates histograms
superimposed with specification limits, parametric density curves, and kernel density estimates; comparative histograms; cdf
plots; probability plots; quantile-quantile plots; and probability-probability plots.
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Reliability Analysis
The RELIABILITY procedure provides methods for reliability and survival data analysis and for recurrence data analysis. These
include fitted life distributions and fitted regression models (including accelerated life test models) for left-, right-, and
interval-censored lifetime data; maximum likelihood estimates of distribution parameters, percentiles and reliability functions;
asymptotic normal and likelihood ratio confidence intervals for parameters and percentiles; Weibayes analyses; nonparametric
estimates and confidence intervals for the mean cumulative function for cost or repairs; analysis of multiple failure models. The
RELIABILITY procedure also creates a variety of probability plots for lifetime data (including Weibull plots), plots for
accelerated life test data, and nonparametric plots of the mean cumulative function and associated confidence intervals.
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Statistical Process Control
The SHEWHART procedure creates a comprehensive set of Shewhart charts, including control charts for means, means and ranges, means
and standard deviations, medians, individual measurements, proportions nonconforming (p charts), numbers nonconforming (np
charts), rates of nonconformities (u charts), and numbers of nonconformities (c charts). You can request Western Electric rules
(tests for special causes, runs tests) for Shewhart charts. You can also use the SHEWHART procedure to create specialized control
charts, including multivariate control charts and control charts for autocorrelated data. The SHEWHART procedure provides
extensive facilities for enhancing control charts graphically, including adding box-and-whisker plots and stars to means charts.
Distinct sets of control limits can be displayed for phases in historical control charts. Control limits can be saved and reused
with new measurements. The CUSUM procedure creates cumulative sum control charts for means and individual measurements. Control
schemes can be based on average run length or V-mask parameters. The EWMA procedure creates moving average and exponentially
weighted moving average charts for means and individual measurements. Control chart parameters can be based on average run
length.
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Analysis of Means
The ANOM procedure creates analysis of means (ANOM) charts for identifying group or treatment means that differ signicantly from
the overall mean. ANOM differs from ANOVA in that ANOVA only determines whether there is a significant difference in treatment
effects. The ANOM procedure can be applied to count data with a Poisson or binomial distribution, as well as continuous data with
a normal distribution. Exact decision limits are computed for both equal and unequal sample sizes. The ANOM procedure provides
extensive features for enhancing and modifying ANOM charts.
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