Providing software solutions since 1976
SAS/QC Software
The ANOM Procedure
- Example 4.1: ANOM Boxcharts with Unequal Group Sizes
- Example 4.2: ANOM p Charts with Angled Axis Labels
- Example 4.3: ANOM u Charts with Angled Axis Labels
- Example 4.4: ANOM Charts with Unequal Group Sizes
- Example 4.5: ANOM for a Two-Way Classification
- Example 4.6: ANOM Charts Using LIMITS= Data Set
- Example 4.7: ANOM for Cell Means in Presence of Interaction
The Capability Procedure
- Example 5.1: Reading Specification Limits
- Example 5.2: Enhancing Reference Lines
- Example 5.3: Displaying a Confidence Interval for Cpk
- Example 5.4: Fitting a Normal Distribution
- Example 5.5: Using Reference Lines with CDF Plots
- Example 5.6: Adding Insets with Descriptive Statistics
- Example 5.7: Creating a Two-Way Comparative Histogram
- Example 5.8: Fitting a Beta Curve
- Example 5.9: Fitting Lognormal, Weibull, and Gamma Curves
- Example 5.10: Comparing Goodness-of-Fit Tests
- Example 5.11: Computing Capability Indices for Nonnormal Distributions
- Example 5.12: Computing Kernel Density Estimates
- Example 5.13: Fitting a Three-Parameter Lognormal Curve
- Example 5.14: Annotating a Folded Normal Curve
- Example 5.15: Inset for Goodness-of-Fit Statistics
- Example 5.16: Inset for Areas Under a Fitted Curve
- Example 5.17: Computing Nonstandard Capability Indices
- Example 5.18: Approximate Confidence Limits for Cpk
- Example 5.19: Displaying a Normal Reference Line
- Example 5.20: Displaying a Lognormal Reference Line
- Example 5.21: Interpreting a Normal Q-Q Plot of Nonnormal Data
- Example 5.22: Estimating Parameters from Lognormal Plots
- Example 5.23: Comparing Weibull Q-Q Plots
- Example 5.24: Estimating Cpk from a Normal Q-Q Plot
The CUSUM Procedure
- Example 6.1: Cusum and Standard Deviation Charts
- Example 6.2: Upper and Lower One-Sided Cusum Charts
- Example 6.3: Combined Shewhart-Cusum Scheme
The FACTEX Procedure
- Example of a Two-Level Full Factorial Design
- Example of a Full Factorial Design in Two Blocks
- Example of a Half-Fraction Factorial Design
- Completely Randomized Design
- Resolution IV Augmented Design
- Factorial Design with Center Points
- Fold-Over Design
- Randomized Complete Block Design
- Two-Level Design with Design Replication and Point Replication
- Mixed-Level Design Using Design Replication and Point Replication
- Mixed-Level Design Using Pseudo-factors
- Mixed-Level Design by Collapsing Factors
- Hyper-Graeco-Latin Square Design
- Resolution IV Design with Minimum Abberation
- Replicated Blocked Design with Partial Confounding
- Incomplete Block Design
- Design with Inner Array and Outer Array
- Fractional Factorial Split-Plot Designs
- A Design for a Three-Step Process
- A Strip-Split-Split-Plot Design
- Design and Analysis of a Complete Factorial Experiment
The ISHIKAWA Procedure
The MACONTROL Procedure
- Example 9.1: Specifying Standard Values for the Process Mean and Process Standard Deviation
- Example 9.2: Displaying Limits Based on Asymptotic Values
- Example 9.3: Working with Unequal Subgroup Sample Sizes
- Example 9.4: Displaying Individual Measurements on an EWMA Chart
- Example 9.5: Computing Average Run Lengths
- Example 9.6: Specifying Standard Values for the Process Mean and Process Standard Deviation
- Example 9.7: Annotating Average Run Lengths on the Chart
The MVPDIAGNOSE Procedure
The MVPMODEL Procedure
- Example 12.1: Using Cross Validation to Select the Number of Principal Components
- Example 12.2: Computing the Classical
The MVPMONITOR Procedure
- Example 13.1: Combining Data from Peer Processes
- Example 13.2: Creating Multivariate Control Charts for Phase II
- Example 13.3: Comparison of Univariate and Multivariate Control Charts
- Example 13.4: Creating a Classical
The OPTEX Procedure
- Nonstandard Linear Model
- Comparing Fedorov Algorithm to Sequential Algorithm
- Using an Initial Design to Search an Optimal Design
- Optimal Design Using an Augmented Best Design
- Optimal Design Using a Small Candidate Set
- Bayesian Optimal Design
- Balanced Incomplete Block Design
- Optimal Design with Fixed Covariates
- Optimal Design in the Presence of Covariates
- Adding Space-Filling Points to a Design
The PARETO Procedure
- Example 15.1: Creating Before-and-After Pareto Charts
- Example 15.2: Creating Two-Way Comparative Pareto Charts
- Example 15.3: Highlighting the
- Example 15.4: Highlighting Combinations of Categories
- Example 15.5: Highlighting Combinations of Cells
- Example 15.6: Ordering Rows and Columns in a Comparative Pareto Chart
- Example 15.7: Merging Columns in a Comparative Pareto Chart
- Example 15.8: Creating Weighted Pareto Charts
- Example 15.9: Alternative Pareto Charts
The RELIABILITY Procedure
- Analysis of Right-Censored Data from a Single Population
- Weibull Analysis Comparing Groups of Data
- Analysis of Accelerated Life Test Data
- Weibull Analysis of Interval Data with Common Inspection Schedule
- Lognormal Analysis with Arbitrary Censoring
- Regression Modeling
- Regression Model with Nonconstant Scale
- Regression Model with Two Independent Variables
- Weibull Probability Plot for Two Combined Failure Modes
- Analysis of Recurrence Data on Repairs
- Comparison of Two Samples of Repair Data
- Analysis of Interval Age Recurrence Data
- Analysis of Binomial Data
- Three-Parameter Weibull
- Parametric Model for Recurrent Events Data
- Parametric Model for Interval Recurrent Events Data
The SHEWHART Procedure
- Example 17.1: Using Box Charts to Compare Subgroups
- Example 17.2: Creating Various Styles of Box-and-Whisker Plots
- Example 17.3: Creating Notched Box-and-Whisker Plots
- Example 17.4: Creating Box-and-Whisker Plots with Varying Widths
- Example 17.5: Creating Box-and-Whisker Plots with Different Line Styles and Colors
- Example 17.6: Computing the Control Limits for Subgroup Maximums
- Example 17.7: Constructing Multi-Vari Charts
- Example 17.8: Applying Tests for Special Causes
- Example 17.9: Specifying a Known Expected Number of Nonconformities
- Example 17.10: Creating c Charts for Varying Numbers of Units
- Example 17.11: Applying Tests for Special Causes
- Example 17.12: Specifying Standard Values for the Process Mean and Standard Deviation
- Example 17.13: Displaying Distributional Plots in the Margin
- Example 17.14: Controlling Value of Central Line
- Example 17.15: Estimating the Process Standard Deviation
- Example 17.16: Working with Unequal Subgroup Sample Sizes
- Example 17.17: Specifying Axis Labels
- Example 17.18: Applying Tests for Special Causes
- Example 17.19: Specifying Standard Average Proportion
- Example 17.20: Working with Unequal Subgroup Sample Sizes
- Example 17.21: Specifying Control Limit Information
- Example 17.22: Applying Tests for Special Causes
- Example 17.23: Specifying Standard Average Proportion
- Example 17.24: Working with Unequal Subgroup Sample Sizes
- Example 17.25: Creating a Chart with Revised Control Limits
- Example 17.26: OC Curve for Chart
- Example 17.27: Computing Probability Limits
- Example 17.28: Specifying Control Limit Information
- Example 17.29: Specifying a Known Standard Deviation
- Example 17.30: Computing Average Run Lengths for s Charts
- Example 17.31: Applying Tests for Special Causes
- Example 17.32: Specifying a Known Expected Number of Nonconformities
- Example 17.33: Creating u Charts for Varying Numbers of Units
- Example 17.34: Applying Tests for Special Causes
- Example 17.35: Estimating the Process Standard Deviation
- Example 17.36: Plotting OC Curves for Mean Charts
- Example 17.37: Computing Process Capability Indices
- Example 17.38: Applying Tests for Special Causes
- Example 17.39: Specifying Standard Values for the Process Mean and Standard Deviation
- Example 17.40: Working with Unequal Subgroup Sample Sizes
- Example 17.41: Specifying Probability Limits
- Example 17.42: Computing Subgroup Summary Statistics
- Example 17.43: Analyzing Nonnormal Process Data
