SAS/QC® Software Features

Basic quality problem solving

  • Pareto charts.
  • Ishikawa diagrams.

Statistical process control (SPC)

  • Shewhart charts: X and R charts, x and x charts, box charts, p charts, np charts, c charts, u charts, individual measurements and moving range charts, tests for special causes.
  • Cumulative sum control charts.
  • Moving average charts.
  • Nonstandard control charts:
    • Trend charts for time-dependent data.
    • Start charts for multivariate process data.
  • Multivariate process modeling and monitoring.
  • Alternative methods for constructing control limits.
  • Westgard rules for laboratory quality control.

GAGE application

  • Measurement system evaluation: range charts, average charts and new macros.
  • Variance components method.

Process capability analysis

  • Comparative histograms.
  • CDF plots, probability plots, Q-Q plots, P-P plots.
  • Capability indices.
  • Confidence, tolerance and prediction intervals.
  • Descriptive statistics.

Reliability analysis

  • Accelerated life test models for censored data.
  • Maximum likelihood estimation.
  • Asymptotic normal and likelihood ratio confidence intervals.
  • Weibayes analyses.
  • Nonparametric estimates and confidence intervals.
  • Analysis of multiple failure models.
  • Probability plots.
  • Life vs. stress plots.
  • Nonparametric plots of mean cumulative function.
  • Extended set of models and new graphics for recurrent event analysis.

Analysis of means

  • Simultaneously compare k treatment means with their overall mean.
  • Single or multiple response variables.
  • Compute decision limits from data.
  • Adjust decision limits for unequal sample sizes.
  • Means charts, p charts, u charts, box charts.

Design of experiments

  • Full and fractional factorial designs.
  • D-optimal and A-optimal designs.
  • ADX Interface for Design of Experiments:
    • Two-level, response surface, mixture and mixed-level designs.
    • Split-plot and fractional factorial split-plot designs.
    • Orthogonal arrays for mixed-level design.
    • Analysis of unstructured experiments.
    • Main effect, interaction, cube and factorial plots.
    • Statistical analyses including regression, ANOVA, residual and outlier analysis.
    • Graphical optimization.
    • HTML report generation.

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