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SAS/STAT® Software Examples
The following SAS/STAT software examples are grouped according to the type of statistical analysis that is being performed. Within each category of statistical analysis, the examples are grouped by the SAS/STAT procedure that is being demonstrated.
Statistical Analysis Categories
- Analysis of Variance
- Bayesian Analysis
- Categorical Data Analysis
- Cluster Analysis
- Descriptive Statistics
- Discriminant Analysis
- Distribution Analysis
- Exact Methods
- Group Sequential Design and Analysis
- Market Research
- Missing Value Imputation
- Mixed Models
- Multivariate Analysis
- Nonparametric Statistics
- Power and Sample Size
- Psychometric Analysis
- Regression
- Spatial Analysis
- Structural Equations
- Survey Sampling and Analysis
- Survival Analysis
- Transformations
- Output Delivery System
- ODS Statistical Graphics
Analysis of Variance
The ANOVA Procedure
- Randomized Complete Block With Factorial Treatment Structure
- Alternative Multiple Comparison Procedures
- Split Plot
- Latin Square Split Plot
- Strip-Split Plot
The CATMOD Procedure
- Linear Response Function, r=2 Responses
- Mean Score Response Function, r=3 Responses
- Logistic Regression, Standard Response Function
- Log-Linear Model, Three Dependent Variables
- Log-Linear Model, Structural and Sampling Zeros
- Repeated Measures, 2 Response Levels, 3 Populations
- Repeated Measures, 4 Response Levels, 1 Population
- Repeated Measures, Logistic Analysis of Growth Curve
- Repeated Measures, Two Repeated Measurement Factors
- Direct Input of Response Functions and Covariance Matrix
- Predicted Probabilities
The GENMOD Procedure
- Logistic Regression
- Normal Regression, Log Link
- Gamma Distribution Applied to Life Data
- Ordinal Model for Multinomial Data
- GEE for Binary Data with Logit Link Function
- Log Odds Ratios and the ALR Algorithm
- Log-Linear Model for Count Data
- Model Assessment of Multiple Regression Using Aggregates of Residuals
- Assessment of a Marginal Model for Dependent Data
- Bayesian Analysis of a Poisson Regression Model
- Exact Poisson Regression
The GLIMMIX Procedure
- Binomial Counts in Randomized Blocks
- Mating Experiment with Crossed Random Effects
- Smoothing Disease Rates; Standardized Mortality Ratios
- Quasi-likelihood Estimation for Proportions with Unknown Distribution
- Joint Modeling of Binary and Count Data
- Radial Smoothing of Repeated Measures Data
- Isotonic Contrasts for Ordered Alternatives
- Adjusted Covariance Matrices of Fixed Effects
- Testing Equality of Covariance and Correlation Matrices
- Multiple Trends Correspond to Multiple Extrema in Profile Likelihoods
- Maximum Likelihood in Proportional Odds Model with Random Effects
- Fitting a Marginal (GEE-Type) Model
- Response Surface Comparisons with Multiplicity Adjustments
- Generalized Poisson Mixed Model for Overdispersed Count Data
- Comparing Multiple B-Splines
- Diallel Experiment with Multimember Random Effects
- Linear Inference Based on Summary Data
The GLM Procedure
- Randomized Complete Blocks with Means Comparisons and Contrasts
- Regression with Mileage Data
- Unbalanced ANOVA for Two-Way Design with Interaction
- Analysis of Covariance
- Three-Way Analysis of Variance with Contrasts
- Multivariate Analysis of Variance
- Repeated Measures Analysis of Variance
- Mixed Model Analysis of Variance with the RANDOM Statement
- Analyzing a Doubly Multivariate Repeated Measures Design
- Testing for Equal Group Variances
- Analysis of a Screening Design
The GLMMOD Procedure
The GLMSELECT Procedure
- Modeling Baseball Salaries Using Performance Statistics
- Using Validation and Cross Validation
- Scatter Plot Smoothing by Selecting Spline Functions
- Multimember Effects and the Design Matrix
- Model Averaging
The INBREED Procedure
The LATTICE Procedure
The MIXED Procedure
- Split-Plot Design
- Repeated Measures
- Plotting the Likelihood
- Known G and R
- Random Coefficients
- Line-Source Sprinkler Irrigation
- Influence in Heterogeneous Variance Model
- Influence Analysis for Repeated Measures Data
- Examining Individual Test Components
- Isotonic Contrasts for Ordered Mean Values
The NESTED Procedure
The NPAR1WAY Procedure
- Two-Sample Location Tests and Plots
- EDF Statistics and EDF Plot
- Exact Wilcoxon Two-Sample Test
- Hodges-Lehmann Estimation
- Exact Savage Multisample Test
The PLAN Procedure
- A Split-Plot Design
- A Hierarchical Design
- An Incomplete Block Design
- A Latin Square Design
- A Generalized Cyclic Incomplete Block Design
- Permutations and Combinations
- Crossover Designs
The TRANSREG Procedure
- Transformation Regression of Exhaust Emissions Data
- Box-Cox Transformations
- Penalized B-Spline
- Nonmetric Conjoint Analysis of Tire Data
- Metric Conjoint Analysis of Tire Data
- Preference Mapping of Automobile Data
The TTEST Procedure
- Using Summary Statistics to Compare Group Means
- One-Sample Comparison with the FREQ Statement
- Paired Comparisons
- AB/BA Crossover Design
- Equivalence Testing with Lognormal Data
The VARCOMP Procedure
Bayesian Analysis
The FMM Procedure (Experimental)
- Mixture Modeling for Binomial Overdispersion: "Student," Pearson, Beer, and Yeast
- Modeling Zero-Inflation: Is it Better to Fish Poorly or Not to Have Fished At All?
The GENMOD Procedure
The LIFEREG Procedure
The MCMC Procedure
- Simulating Samples From a Known Density
- Box-Cox Transformation
- Logistic Regression Model with a Diffuse Prior
- Logistic Regression Model with Jeffrey's Prior
- Poisson Regression
- Nonlinear Poisson Regression Models
- Logistic Regression Random-Effects Models
- Nonlinear Poisson Regression Random-Effects Model
- Multivariate Normal Random-Effects Model
- Change Point Models
- Exponential and Weibull Survival Analysis
- Time Independent Cox Models
- Time Dependent Cox Models
- Piecewise Exponential Frailty Model
- Normal Regression with Interval Censoring
- Constrained Analysis
- Implement a New Sampling Algorithm
- Using a Transformation to Improve Mixing
- Gelman-Rubin Diagnostics
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Bayesian Zero-Inflated Poisson Regression
PDF (616KB) | HTML -
Bayesian Linear Regression with Standardized Covariates
PDF (307KB) | HTML -
Bayesian Hierarchical Modeling for Meta-Analysis
PDF (348KB) | HTML
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Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.2
PDF (348KB) | HTML
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Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.3
PDF (348KB) | HTML
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Bayesian Binomial Model with Power Prior Using the MCMC Procedure
PDF (348KB) | HTML
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Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.2
PDF (348KB) | HTML
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Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.3
PDF (348KB) | HTML
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Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.2
PDF (348KB) | HTML
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Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.3
PDF (348KB) | HTML
The PHREG Procedure
Categorical Data Analysis
The CATMOD Procedure
- Linear Response Function, r=2 Responses
- Mean Score Response Function, r=3 Responses
- Logistic Regression, Standard Response Function
- Log-Linear Model, Three Dependent Variables
- Log-Linear Model, Structural and Sampling Zeros
- Repeated Measures, 2 Response Levels, 3 Populations
- Repeated Measures, 4 Response Levels, 1 Population
- Repeated Measures, Logistic Analysis of Growth Curve
- Repeated Measures, Two Repeated Measurement Factors
- Direct Input of Response Functions and Covariance Matrix
- Predicted Probabilities
The CORRESP Procedure
- Simple and Multiple Correspondence Analysis of Automobiles and Their Owners
- Simple Correspondence Analysis of U.S. Population
The FREQ Procedure
- Output Data Set of Frequencies
- Frequency Dot Plots
- Chi-Square Goodness-of-Fit Tests
- Binomial Proportions
- Analysis of a 2x2 Contingency Table
- Output Data Set of Chi-Square Statistics
- Cochran-Mantel-Haenszel Statistics
- Cochran-Armitage Trend Test
- Friedman’s Chi-Square Test
- Cochran’s Q Test
The GAM Procedure
- Generalized Additive Model with Binary Data
- Poisson Regression Analysis of Component Reliability
- Comparing PROC GAM with PROC LOESS
The GENMOD Procedure
- Logistic Regression
- Gamma Distribution Applied to Life Data
- Ordinal Model for Multinomial Data
- GEE for Binary Data with Logit Link Function
- Log Odds Ratios and the ALR Algorithm
- Log-Linear Model for Count Data
- Assessment of a Marginal Model for Dependent Data
- Exact Poisson Regression
The GLIMMIX Procedure
- Binomial Counts in Randomized Blocks
- Joint Modeling of Binary and Count Data
- Isotonic Contrasts for Ordered Alternatives
- Fitting a Marginal (GEE-Type) Model
- Response Surface Comparisons with Multiplicity Adjustments
- Generalized Poisson Mixed Model for Overdispersed Count Data
The LOGISTIC Procedure
- Stepwise Logistic Regression and Predicted Values
- Logistic Modeling with Categorical Predictors
- Ordinal Logistic Regression
- Nominal Response Data: Generalized Logits Model
- Stratified Sampling
- Logistic Regression Diagnostics
- ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
- Comparing Receiver Operating Characteristic Curves
- Goodness-of-Fit Tests and Subpopulations
- Overdispersion
- Conditional Logistic Regression for Matched Pairs Data
- Firth’s Penalized Likelihood Compared with Other Approaches
- Scoring Data Sets
- Using the LSMEANS Statement
The PRINQUAL Procedure
The PROBIT Procedure
The SURVEYFREQ Procedure
The SURVEYLOGISTIC Procedure
The TRANSREG Procedure
- Nonmetric Conjoint Analysis of Tire Data
- Metric Conjoint Analysis of Tire Data
- Preference Mapping of Automobile Data
Cluster Analysis
The ACECLUS Procedure
The CLUSTER Procedure
- Cluster Analysis of Flying Mileages between 10 American Cities
- Crude Birth and Death Rates
- Cluster Analysis of Fisher’s Iris Data
- Evaluating the Effects of Ties
The FASTCLUS Procedure
The MODECLUS Procedure
- Cluster Analysis of Samples from Univariate Distributions
- Cluster Analysis of Flying Mileages between Ten American Cities
- Cluster Analysis with Significance Tests
- Cluster Analysis: Hertzsprung-Russell Plot
- Using the TRACE Option When METHOD=6
The TREE Procedure
The VARCLUS Procedure
Descriptive Statistics
The BOXPLOT Procedure
- Displaying Summary Statistics in a Box Plot
- Using Box Plots to Compare Groups
- Creating Various Styles of Box-and-Whiskers Plots
- Creating Notched Box-and-Whiskers Plots
- Creating Box-and-Whiskers Plots with Varying Widths
- Creating Box-and-Whiskers Plots Using ODS Graphics
The SURVEYMEANS Procedure
- Stratified Cluster Sample Design
- Domain Analysis
- Ratio Analysis
- Analyzing Survey Data with Missing Values
- Variance Estimation Using Replication Methods
Discriminant Analysis
The CANDISC Procedure
The DISCRIM Procedure
- Univariate Density Estimates and Posterior Probabilities
- Bivariate Density Estimates and Posterior Probabilities
- Normal-Theory Discriminant Analysis of Iris Data
- Linear Discriminant Analysis of Remote-Sensing Data on Crops
The STEPDISC Procedure
Distribution Analysis
The KDE Procedure
- Computing a Basic Kernel Density Estimate
- Changing the Bandwidth
- Changing the Bandwidth (Bivariate)
- Requesting Additional Output Tables
- Univariate KDE Graphics
- Bivariate KDE Graphics
Exact Methods
The FREQ Procedure
The GENMOD Procedure
The LOGISTIC Procedure
- Conditional Logistic Regression for Matched Pairs Data
- Firth’s Penalized Likelihood Compared with Other Approaches
The MULTTEST Procedure
- Cochran-Armitage Test with Permutation Resampling
- Peto Mortality-Prevalence Test
- Fisher Test with Permutation Resampling
The NPAR1WAY Procedure
Group Sequential Design and Analysis
The SEQDESIGN Procedure
- Creating Fixed-Sample Designs
- Creating a One-Sided O’Brien-Fleming Design
- Creating Two-Sided Pocock and O’Brien-Fleming Designs
- Generating Graphics Display for Sequential Designs
- Creating Designs Using Haybittle-Peto Methods
- Creating Designs with Various Stopping Criteria
- Creating Whitehead’s Triangular Designs
- Creating a One-Sided Error Spending Design
- Creating Designs with Various Number of Stages
- Creating Two-Sided Error Spending Designs with and without Overlapping Lower and Upper Beta Boundaries
- Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject Null Hypothesis
- Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject or Accept Null Hypothesis
The SEQTEST Procedure
- Testing the Difference between Two Proportions
- Testing an Effect in a Regression Model
- Testing an Effect with Early Stopping to Accept Null Hypothesis
- Testing a Binomial Proportion
- Comparing Two Proportions with a Log Odds Ratio Test
- Comparing Two Survival Distributions with a Log-Rank Test
- Testing an Effect in a Proportional Hazards Regression Model
- Testing an Effect in a Logistic Regression Model
Market Research
The CORRESP Procedure
The PRINQUAL Procedure
The TRANSREG Procedure
- Nonmetric Conjoint Analysis of Tire Data
- Metric Conjoint Analysis of Tire Data
- Preference Mapping of Automobile Data
Missing Value Imputation
The MI Procedure
- Generating the Data Sets used in the MI Procedure Examples
- EM Algorithm for MLE
- Monotone Propensity Score Method
- Monotone Regression Method
- Monotone Logistic Regression Method for CLASS Variables
- Monotone Discriminant Function Method for CLASS Variables
- FCS Method for Continuous Variables
- FCS Method for CLASS Variables
- FCS Method with Trace Plot
- MCMC Method
- Producing Monotone Missingness with MCMC
- Checking Convergence in MCMC
- Saving and Using Parameters for MCMC
- Transforming to Normality
- Multistage Imputation
The MIANALYZE Procedure
- Generating the Data Sets used in the MIANALYZE Procedure Examples
- Reading Means and Standard Errors from Variables in a DATA= Data Set
- Reading Means and Covariance Matrices from a DATA= COV Data Set
- Reading Regression Results from a DATA= EST Data Set
- Reading Mixed Model Results from PARMS= and COVB= Data Sets
- Reading Generalized Linear Model Results
- Reading GLM Results from PARMS= and XPXI= Data Sets
- Reading Logistic Model Results from PARMS= and COVB= Data Sets
- Reading Mixed Model Results with Classification Variables
- Using a TEST statement
- Combining Correlation Coefficients
Mixed Models
The GLIMMIX Procedure
- Binomial Counts in Randomized Blocks
- Mating Experiment with Crossed Random Effects
- Smoothing Disease Rates; Standardized Mortality Ratios
- Quasi-likelihood Estimation for Proportions with Unknown Distribution
- Joint Modeling of Binary and Count Data
- Radial Smoothing of Repeated Measures Data
- Isotonic Contrasts for Ordered Alternatives
- Adjusted Covariance Matrices of Fixed Effects
- Testing Equality of Covariance and Correlation Matrices
- Multiple Trends Correspond to Multiple Extrema in Profile Likelihoods
- Maximum Likelihood in Proportional Odds Model with Random Effects
- Fitting a Marginal (GEE-Type) Model
- Response Surface Comparisons with Multiplicity Adjustments
- Generalized Poisson Mixed Model for Overdispersed Count Data
- Comparing Multiple B-Splines
- Diallel Experiment with Multimember Random Effects
- Linear Inference Based on Summary Data
The HPMIXED Procedure
- Ranking Many Random-Effect Coefficients
- Comparing Results from PROC HPMIXED and PROC MIXED
- Using PROC GLIMMIX for Further Analysis of PROC HPMIXED Fit
- Mixed Model Analysis of Microarray Data
- Repeated Measures
The MIXED Procedure
- Split-Plot Design
- Repeated Measures
- Plotting the Likelihood
- Known G and R
- Random Coefficients
- Line-Source Sprinkler Irrigation
- Influence in Heterogeneous Variance Model
- Influence Analysis for Repeated Measures Data
- Examining Individual Test Components
- Isotonic Contrasts for Ordered Mean Values
The NLMIXED Procedure
- One-Compartment Model with Pharmacokinetic Data
- Probit-Normal Model with Binomial Data
- Probit-Normal Model with Ordinal Data
- Poisson-Normal Model with Count Data
- Failure Time and Frailty Model
Multivariate Analysis
The CALIS Procedure
- Estimating Covariances and Correlations
- Estimating Covariances and Means Simultaneously
- Testing Uncorrelatedness of Variables
- Testing Covariance Patterns
- Testing Some Standard Covariance Pattern Hypotheses
- Linear Regression Model
- Multivariate Regression Models
- Measurement Error Models
- Testing Specific Measurement Error Models
- Measurement Error Models with Multiple Predictors
- Measurement Error Models Specified As Linear Equations
- Confirmatory Factor Models
- Confirmatory Factor Models: Some Variations
- The Full Information Maximum Likelihood Method
- Comparing the ML and FIML Estimation
- Path Analysis: Stability of Alienation
- Simultaneous Equations with Mean Structures and Reciprocal Paths
- Fitting Direct Covariance Structures
- Confirmatory Factor Analysis: Cognitive Abilities
- Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis
- Testing Equality of Covariance and Mean Matrices Between Independent Groups
- Illustrating Various General Modeling Languages
- Testing Competing Path Models for the Career Aspiration Data
- Fitting a Latent Growth Curve Model
- Higher-Order and Hierarchical Factor Models
- Linear Relations among Factor Loadings
- Multiple-Group Model for Purchasing Behavior
- Fitting the RAM and EQS Models by the COSAN Modeling Language
- Second-Order Confirmatory Factor Analysis
- Linear Relations among Factor Loadings: COSAN Model Specification
- Ordinal Relations among Factor Loadings
- Longitudinal Factor Analysis
The CANCORR Procedure
The CORRESP Procedure
- Simple and Multiple Correspondence Analysis of Automobiles and Their Owners
- Simple Correspondence Analysis of U.S. Population
The FACTOR Procedure
- Principal Component Analysis
- Principal Factor Analysis
- Maximum Likelihood Factor Analysis
- Using Confidence Intervals to Locate Salient Factor Loadings
The MDS Procedure
The MULTTEST Procedure
- Cochran-Armitage Test with Permutation Resampling
- Freeman-Tukey and t Tests with Bootstrap Resampling
- Peto Mortality-Prevalence Test
- Fisher Test with Permutation Resampling
- Inputting Raw p-Values
- Adaptive Adjustments and ODS Graphics
The PLS Procedure
- Examining Model Details
- Examining Outliers
- Choosing a PLS Model by Test Set Validation
- Partial Least Squares Spline Smoothing
The PRINCOMP Procedure
The PRINQUAL Procedure
The TRANSREG Procedure
- Transformation Regression of Exhaust Emissions Data
- Box-Cox Transformations
- Penalized B-Spline
- Nonmetric Conjoint Analysis of Tire Data
- Metric Conjoint Analysis of Tire Data
- Preference Mapping of Automobile Data
The TREE Procedure
Nonparametric Statistics
The FREQ Procedure
The GAM Procedure
- Generalized Additive Model with Binary Data
- Poisson Regression Analysis of Component Reliability
- Comparing PROC GAM with PROC LOESS
The KDE Procedure
- Computing a Basic Kernel Density Estimate
- Changing the Bandwidth
- Changing the Bandwidth (Bivariate)
- Requesting Additional Output Tables
- Univariate KDE Graphics
- Bivariate KDE Graphics
The LOESS Procedure
- Engine Exhaust Emissions
- Sulfate Deposits in the U.S. for 1990
- Catalyst Experiment
- El Niño Southern Oscillation
The NPAR1WAY Procedure
- Two-Sample Location Tests and Plots
- EDF Statistics and EDF Plot
- Exact Wilcoxon Two-Sample Test
- Hodges-Lehmann Estimation
- Exact Savage Multisample Test
The TPSPLINE Procedure
- Partial Spline Model Fit
- Spline Model with Higher-Order Penalty
- Multiple Minima of the GCV Function
- Large Data Set Application
- Computing a Bootstrap Confidence Interval
Power and Sample Size
The GLMPOWER Procedure
The POWER Procedure
- One-Way ANOVA
- The Sawtooth Power Function in Proportion Analyses
- Simple AB/BA Crossover Designs
- Noninferiority Test with Lognormal Data
- Multiple Regression and Correlation
- Comparing Two Survival Curves
- Confidence Interval Precision
- Customizing Plots
- Binary Logistic Regression with Independent Predictors
- Wilcoxon-Mann-Whitney Test
Psychometric Analysis
The CORRESP Procedure
- Simple and Multiple Correspondence Analysis of Automobiles and Their Owners
- Simple Correspondence Analysis of U.S. Population
The FACTOR Procedure
- Principal Component Analysis
- Principal Factor Analysis
- Maximum Likelihood Factor Analysis
- Using Confidence Intervals to Locate Salient Factor Loadings
The MDS Procedure
The PRINCOMP Procedure
The TRANSREG Procedure
- Transformation Regression of Exhaust Emissions Data
- Box-Cox Transformations
- Penalized B-Spline
- Nonmetric Conjoint Analysis of Tire Data
- Metric Conjoint Analysis of Tire Data
- Preference Mapping of Automobile Data
Regression
The CATMOD Procedure
- Linear Response Function, r=2 Responses
- Mean Score Response Function, r=3 Responses
- Logistic Regression, Standard Response Function
- Log-Linear Model, Three Dependent Variables
- Log-Linear Model, Structural and Sampling Zeros
- Repeated Measures, 2 Response Levels, 3 Populations
- Repeated Measures, 4 Response Levels, 1 Population
- Repeated Measures, Logistic Analysis of Growth Curve
- Repeated Measures, Two Repeated Measurement Factors
- Direct Input of Response Functions and Covariance Matrix
- Predicted Probabilities
The FMM Procedure (Experimental)
- Mixture Modeling for Binomial Overdispersion: "Student," Pearson, Beer, and Yeast
- Modeling Zero-Inflation: Is it Better to Fish Poorly or Not to Have Fished At All?
- Looking for Multiple Modes: Are Galaxies Clustered?
- Modeling Mixing Probabilities: All Mice Are Created Equal, but Some Are More Equal
- The Usefulness of Custom Starting Values: When Do Cows Eat?
- Enforcing Homogeneity Constraints: Count and Dispersion–It Is All Over!
The GAM Procedure
- Generalized Additive Model with Binary Data
- Poisson Regression Analysis of Component Reliability
- Comparing PROC GAM with PROC LOESS
The GENMOD Procedure
- Logistic Regression
- Normal Regression, Log Link
- Gamma Distribution Applied to Life Data
- Ordinal Model for Multinomial Data
- GEE for Binary Data with Logit Link Function
- Log Odds Ratios and the ALR Algorithm
- Log-Linear Model for Count Data
- Model Assessment of Multiple Regression Using Aggregates of Residuals
- Assessment of a Marginal Model for Dependent Data
- Bayesian Analysis of a Poisson Regression Model
- Exact Poisson Regression
The GLIMMIX Procedure
- Binomial Counts in Randomized Blocks
- Mating Experiment with Crossed Random Effects
- Smoothing Disease Rates; Standardized Mortality Ratios
- Quasi-likelihood Estimation for Proportions with Unknown Distribution
- Joint Modeling of Binary and Count Data
- Radial Smoothing of Repeated Measures Data
- Isotonic Contrasts for Ordered Alternatives
- Adjusted Covariance Matrices of Fixed Effects
- Testing Equality of Covariance and Correlation Matrices
- Multiple Trends Correspond to Multiple Extrema in Profile Likelihoods
- Maximum Likelihood in Proportional Odds Model with Random Effects
- Fitting a Marginal (GEE-Type) Model
- Response Surface Comparisons with Multiplicity Adjustments
- Generalized Poisson Mixed Model for Overdispersed Count Data
- Comparing Multiple B-Splines
- Diallel Experiment with Multimember Random Effects
- Linear Inference Based on Summary Data
The GLM Procedure
- Randomized Complete Blocks with Means Comparisons and Contrasts
- Regression with Mileage Data
- Unbalanced ANOVA for Two-Way Design with Interaction
- Analysis of Covariance
- Three-Way Analysis of Variance with Contrasts
- Multivariate Analysis of Variance
- Repeated Measures Analysis of Variance
- Mixed Model Analysis of Variance with the RANDOM Statement
- Analyzing a Doubly Multivariate Repeated Measures Design
- Testing for Equal Group Variances
- Analysis of a Screening Design
The GLMSELECT Procedure
- Modeling Baseball Salaries Using Performance Statistics
- Using Validation and Cross Validation
- Scatter Plot Smoothing by Selecting Spline Functions
- Multimember Effects and the Design Matrix
- Model Averaging
The LIFEREG Procedure
- Motorette Failure
- Computing Predicted Values for a Tobit Model
- Overcoming Convergence Problems by Specifying Initial Values
- Analysis of Arbitrarily Censored Data with Interaction Effects
- Probability Plotting—Right Censoring
- Probability Plotting—Arbitrary Censoring
- Bayesian Analysis of Clinical Trial Data
The LOESS Procedure
- Engine Exhaust Emissions
- Sulfate Deposits in the U.S. for 1990
- Catalyst Experiment
- El Niño Southern Oscillation
The LOGISTIC Procedure
- Stepwise Logistic Regression and Predicted Values
- Logistic Modeling with Categorical Predictors
- Ordinal Logistic Regression
- Nominal Response Data: Generalized Logits Model
- Stratified Sampling
- Logistic Regression Diagnostics
- ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
- Comparing Receiver Operating Characteristic Curves
- Goodness-of-Fit Tests and Subpopulations
- Overdispersion
- Conditional Logistic Regression for Matched Pairs Data
- Firth’s Penalized Likelihood Compared with Other Approaches
- Complementary Log-Log Model for Infection Rates
- Complementary Log-Log Model for Interval-Censored Survival Times
- Scoring Data Sets
- Using the LSMEANS Statement
The MIXED Procedure
- Split-Plot Design
- Repeated Measures
- Plotting the Likelihood
- Known G and R
- Random Coefficients
- Line-Source Sprinkler Irrigation
- Influence in Heterogeneous Variance Model
- Influence Analysis for Repeated Measures Data
- Examining Individual Test Components
- Isotonic Contrasts for Ordered Mean Values
The NLIN Procedure
- Segmented Model
- Iteratively Reweighted Least Squares
- Probit Model with Likelihood Function
- Affecting Curvature through Parameterization
- Comparing Nonlinear Trends among Groups
- ODS Graphics and Diagnostics
The NLMIXED Procedure
- One-Compartment Model with Pharmacokinetic Data
- Probit-Normal Model with Binomial Data
- Probit-Normal Model with Ordinal Data
- Poisson-Normal Model with Count Data
- Failure Time and Frailty Model
The ORTHOREG Procedure
The PLM Procedure
- Scoring with PROC PLM
- Working with Item Stores
- Group Comparisons in Ordinal Model
- Posterior Inference for Binomial Data
- By-Group Processing
- Comparing Multiple B-Splines
- Linear Inference with Arbitrary Estimates
The PHREG Procedure
- Stepwise Regression
- Best Subset Selection
- Modeling with Categorical Predictors
- Firth’s Correction for Monotone Likelihood
- Conditional Logistic Regression for m:n Matching
- Model Using Time-Dependent Explanatory Variables
- Time-Dependent Repeated Measurements of a Covariate
- Survivor Function Estimates for Specific Covariate Values
- Analysis of Residuals
- Analysis of Recurrent Events Data
- Analysis of Clustered Data
- Model Assessment Using Cumulative Sums of Martingale Residuals
- Bayesian Analysis of the Cox Model
- Bayesian Analysis of Piecewise Exponential Model
The PLS Procedure
- Examining Model Details
- Examining Outliers
- Choosing a PLS Model by Test Set Validation
- Partial Least Squares Spline Smoothing
The PROBIT Procedure
The QUANTREG Procedure
- Comparison of Algorithms
- Quantile Regression for Econometric Growth Data
- Quantile Regression Analysis of Birth-Weight Data
- Nonparametric Quantile Regression for Ozone Levels
- Quantile Polynomial Regression for Salary Data
The REG Procedure
- Modeling Salaries of Major League Baseball Players
- Aerobic Fitness Prediction
- Predicting Weight by Height and Age
- Regression with Quantitative and Qualitative Variables
- Ridge Regression for Acetylene Data
- Chemical Reaction Response
The ROBUSTREG Procedure
- Comparison of Robust Estimates
- Robust ANOVA
- Growth Study of De Long and Summers
- Constructed Effects
- Robust Diagnostics
The RSREG Procedure
The SURVEYLOGISTIC Procedure
The SURVEYREG Procedure
- Simple Random Sampling
- Cluster Sampling
- Regression Estimator for Simple Random Sample
- Stratified Sampling
- Regression Estimator for Stratified Sample
- Stratum Collapse
- Domain Analysis
- Compare Domain Statistics
- Variance Estimate Using the Jackknife Method
The TPSPLINE Procedure
- Partial Spline Model Fit
- Spline Model with Higher-Order Penalty
- Multiple Minima of the GCV Function
- Large Data Set Application
- Computing a Bootstrap Confidence Interval
The TRANSREG Procedure
- Transformation Regression of Exhaust Emissions Data
- Box-Cox Transformations
- Penalized B-Spline
- Nonmetric Conjoint Analysis of Tire Data
- Metric Conjoint Analysis of Tire Data
- Preference Mapping of Automobile Data
Spatial Analysis
The KRIGE2D Procedure
- Spatial Prediction of Pollutant Concentration
- Investigating the Effect of Model Specification on Prediction
- Data Quality and Prediction with Missing Values
The MIXED Procedure
The SIM2D Procedure
The VARIOGRAM Procedure
- Aspects of Semivariogram Model Fitting
- An Anisotropic Case Study with Surface Trend in the Data
- Analysis without Surface Trend Removal
- Covariogram and Semivariogram
- A Box Plot of the Square Root Difference Cloud
Structural Equations
The CALIS Procedure
- Estimating Covariances and Correlations
- Estimating Covariances and Means Simultaneously
- Testing Uncorrelatedness of Variables
- Testing Covariance Patterns
- Testing Some Standard Covariance Pattern Hypotheses
- Linear Regression Model
- Multivariate Regression Models
- Measurement Error Models
- Testing Specific Measurement Error Models
- Measurement Error Models with Multiple Predictors
- Measurement Error Models Specified As Linear Equations
- Confirmatory Factor Models
- Confirmatory Factor Models: Some Variations
- The Full Information Maximum Likelihood Method
- Comparing the ML and FIML Estimation
- Path Analysis: Stability of Alienation
- Simultaneous Equations with Mean Structures and Reciprocal Paths
- Fitting Direct Covariance Structures
- Confirmatory Factor Analysis: Cognitive Abilities
- Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis
- Testing Equality of Covariance and Mean Matrices Between Independent Groups
- Illustrating Various General Modeling Languages
- Testing Competing Path Models for the Career Aspiration Data
- Fitting a Latent Growth Curve Model
- Higher-Order and Hierarchical Factor Models
- Linear Relations among Factor Loadings
- Multiple-Group Model for Purchasing Behavior
- Fitting the RAM and EQS Models by the COSAN Modeling Language
- Second-Order Confirmatory Factor Analysis
- Linear Relations among Factor Loadings: COSAN Model Specification
- Ordinal Relations among Factor Loadings
- Longitudinal Factor Analysis
Survey Sampling and Analysis
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Using Bootstrap Replicate Weights with SAS/STAT Survey Procedures
PDF (148KB) | HTML -
Estimating Geometric Means Using Data from a Complex Survey Sampling Design
PDF (133KB) | HTML -
Estimating the Variance of a Variable in a Finite Population
PDF (133KB) | HTML -
Estimating the Standard Deviation of a Variable in a Finite Population
PDF (133KB) | HTML
The SURVEYFREQ Procedure
The SURVEYLOGISTIC Procedure
The SURVEYMEANS Procedure
- Stratified Cluster Sample Design
- Domain Analysis
- Ratio Analysis
- Analyzing Survey Data with Missing Values
- Variance Estimation Using Replication Methods
The SURVEYPHREG Procedure
- Analysis of Clustered Data
- Stratification, Clustering, and Unequal Weights
- Domain Analysis
- Variance Estimation by Using Replicate Weights
The SURVEYREG Procedure
- Simple Random Sampling
- Cluster Sampling
- Regression Estimator for Simple Random Sample
- Stratified Sampling
- Regression Estimator for Stratified Sample
- Stratum Collapse
- Domain Analysis
- Compare Domain Statistics
- Variance Estimate Using the Jackknife Method
The SURVEYSELECT Procedure
- Replicated Sampling
- PPS Selection of Two Units per Stratum
- PPS (Dollar-Unit) Sampling
- Proportional Allocation
Survival Analysis
The LIFEREG Procedure
- Motorette Failure
- Computing Predicted Values for a Tobit Model
- Overcoming Convergence Problems by Specifying Initial Values
- Analysis of Arbitrarily Censored Data with Interaction Effects
- Probability Plotting—Right Censoring
- Probability Plotting—Arbitrary Censoring
- Bayesian Analysis of Clinical Trial Data
The LIFETEST Procedure
- Product-Limit Estimates and Tests of Association
- Enhanced Survival Plot and Multiple-Comparison Adjustments
- Life-Table Estimates for Males with Angina Pectoris
The PHREG Procedure
- Stepwise Regression
- Best Subset Selection
- Modeling with Categorical Predictors
- Firth’s Correction for Monotone Likelihood
- Conditional Logistic Regression for m:n Matching
- Model Using Time-Dependent Explanatory Variables
- Time-Dependent Repeated Measurements of a Covariate
- Survivor Function Estimates for Specific Covariate Values
- Analysis of Residuals
- Analysis of Recurrent Events Data
- Analysis of Clustered Data
- Model Assessment Using Cumulative Sums of Martingale Residuals
- Bayesian Analysis of the Cox Model
- Bayesian Analysis of Piecewise Exponential Model
The SEQDESIGN Procedure
- Creating Fixed-Sample Designs
- Creating a One-Sided O’Brien-Fleming Design
- Creating Two-Sided Pocock and O’Brien-Fleming Designs
- Generating Graphics Display for Sequential Designs
- Creating Designs Using Haybittle-Peto Methods
- Creating Designs with Various Stopping Criteria
- Creating Whitehead’s Triangular Designs
- Creating a One-Sided Error Spending Design
- Creating Designs with Various Number of Stages
- Creating Two-Sided Error Spending Designs with and without Overlapping Lower and Upper Beta Boundaries
- Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject Null Hypothesis
- Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject or Accept Null Hypothesis
The SEQTEST Procedure
- Testing the Difference Between Two Proportions
- Testing an Effect in a Regression Model
- testing an Effect with Early Stopping to Accept Null Hypothesis
- Testing a Binomial Proportion
- Comparing Two Proportions with a Log Odds Ratio Test
- Comparing Two Survival Distributions with a Log-Rank Test
- Testing an Effect in a Proportional Hazards Regression Model
- Testing an Effect in a Logistic Regression Model
The SURVEYPHREG Procedure
- Analysis of Clustered Data
- Stratification, Clustering, and Unequal Weights
- Domain Analysis
- Variance Estimation by Using Replicate Weights
- A Test of the Proportional Hazards Assumption by Using the Programming Statements
Transformations
The DISTANCE Procedure
The SCORE Procedure
The SIMNORMAL Procedure
The STDIZE Procedure
Output Delivery System
- Creating HTML Output with ODS
- Selecting ODS Tables for Display
- Excluding ODS Tables from Display
- Creating an Output Data Set from an ODS Table
- Creating an Output Data Set: Subsetting the Data
- RUN-Group Processing
- ODS Output Data Sets and Using PROC TEMPLATE to Customize Output
- HTML Output with Hyperlinks between Tables
- HTML Output with Graphics and Hyperlinks
- Correlation and Covariance Matrices
