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SAS/STAT 13.1 User's Guide

Examples from Documentation

Select an example PROC ACECLUS PROC ADAPTIVEREG PROC ANOVA PROC BCHOICE (New) PROC BOXPLOT PROC CALIS PROC CANCORR PROC CANDISC PROC CATMOD PROC CLUSTER PROC CORRESP PROC DISCRIM PROC DISTANCE PROC FACTOR PROC FASTCLUS PROC FMM PROC FREQ PROC GAM PROC GENMOD PROC GLIMMIX PROC GLM PROC GLMMOD PROC GLMPOWER PROC GLMSELECT PROC HPFMM PROC HPMIXED PROC ICLIFETEST (New) PROC INBREED PROC IRT (New) PROC KDE PROC KRIGE2D PROC LATTICE PROC LIFEREG PROC LIFETEST PROC LOESS PROC LOGISTIC PROC MCMC PROC MDS PROC MI PROC MIANALYZE PROC MIXED PROC MODECLUS PROC MULTTEST PROC NESTED PROC NLIN PROC NLMIXED PROC NPAR1WAY PROC ORTHOREG PROC PHREG PROC PLAN PROC PLM PROC PLS PROC POWER PROC PRINCOMP PROC PRINQUAL PROC PROBIT PROC QUANTLIFE PROC QUANTREG PROC QUANTSELECT PROC REG PROC ROBUSTREG PROC RSREG PROC SCORE PROC SEQDESIGN PROC SEQTEST PROC SIM2D PROC SIMNORMAL PROC STDIZE PROC STDRATE PROC STEPDISC PROC SURVEYFREQ PROC SURVEYLOGISTIC PROC SURVEYMEANS PROC SURVEYPHREG PROC SURVEYREG PROC SURVEYSELECT PROC TCALIS PROC TPSPLINE PROC TRANSREG PROC TREE PROC TTEST PROC VARCLUS PROC VARCOMP PROC VARIOGRAM

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ACECLUS Procedure

• Getting Started: ACECLUS Procedure

• Example 24.1: Transformation and Cluster Analysis of Fisher Iris Data

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ADAPTIVEREG Procedure

• Getting Started: ADAPTIVEREG Procedure

• Example 25.1: Surface Fitting with Many Noisy Variables
• Example 25.2: Fitting Data with Mixture Structures
• Example 25.3: Predicting E-Mail Spam
• Example 25.4: Nonparametric Poisson Model for Mackerel Egg Density

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The ANOVA Procedure

• One-Way Layout with Means Comparisons
• Randomized Complete Block with One Factor

• Example 26.1: Randomized Complete Block With Factorial Treatment Structure
• Example 26.2: Alternative Multiple Comparison Procedures
• Example 26.3: Split Plot
• Example 26.4: Latin Square Split Plot
• Example 26.5: Strip-Split Plot

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BCHOICE Procedure New Procedure!

• A Simple Logit Model
• A Logit Model with Random Effects

• Example 27.1: Alternative-Specific and Individual-Specific Effects
• Example 27.2: Nested Logit Modeling
• Example 27.3: Probit Modeling
• Example 27.4: A Random-Effects-Only Logit Model
• Example 27.5: Heterogeneity Affected by Individual Characteristics
• Example 27.6: Inference on Quantities of Interest
• Example 27.7: Predict the Choice Probabilities

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BOXPLOT Procedure

• Creating Box Plots from Raw Data
• Creating Box Plots from Summary Data
• Saving Summary Data with Outliers

• Example 28.1: Displaying Summary Statistics in a Box Plot
• Example 28.2: Using Box Plots to Compare Groups
• Example 28.3: Creating Various Styles of Box-and-Whiskers Plots
• Example 28.4: Creating Notched Box-and-Whiskers Plots
• Example 28.5: Creating Box-and-Whiskers Plots with Varying Widths
• Example 28.6: Creating Box-and-Whiskers Plots Using ODS Graphics

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CALIS Procedure

• A Structural Equation Example
• A Factor Model Example
• Direct Covariance Structures Analysis
• Which Modeling Language?

• Example 29.1: Estimating Covariances and Correlations
• Example 29.2: Estimating Covariances and Means Simultaneously
• Example 29.3: Testing Uncorrelatedness of Variables
• Example 29.4: Testing Covariance Patterns
• Example 29.5: Testing Some Standard Covariance Pattern Hypotheses
• Example 29.6: Linear Regression Model
• Example 29.7: Multivariate Regression Models
• Example 29.8: Measurement Error Models
• Example 29.9: Testing Specific Measurement Error Models
• Example 29.10: Measurement Error Models with Multiple Predictors
• Example 29.11: Measurement Error Models Specified As Linear Equations
• Example 29.12: Confirmatory Factor Models
• Example 29.13: Confirmatory Factor Models: Some Variations
• Example 29.14: Residual Diagnostics and Robust Estimation
• Example 29.15: The Full Information Maximum Likelihood Method
• Example 29.16: Comparing the ML and FIML Estimation
• Example 29.17: Path Analysis: Stability of Alienation New!
• Example 29.18: Simultaneous Equations with Mean Structures and Reciprocal Paths
• Example 29.19: Fitting Direct Covariance Structures
• Example 29.20: Confirmatory Factor Analysis: Cognitive Abilities
• Example 29.21: Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis
• Example 29.22: Testing Equality of Covariance and Mean Matrices between Independent Groups
• Example 29.23: Illustrating Various General Modeling Languages
• Example 29.24: Testing Competing Path Models for the Career Aspiration Data
• Example 29.25: Fitting a Latent Growth Curve Model
• Example 29.26: Higher-Order and Hierarchical Factor Models
• Example 29.27: Linear Relations among Factor Loadings
• Example 29.28: Multiple-Group Model for Purchasing Behavior
• Example 29.29: Fitting the RAM and EQS Models by the COSAN Modeling Language
• Example 29.30: Second-Order Confirmatory Factor Analysis
• Example 29.31: Linear Relations among Factor Loadings: COSAN Model Specification
• Example 29.32: Ordinal Relations among Factor Loadings
• Example 29.33: Longitudinal Factor Analysis

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CANCORR Procedure

• Getting Started: CANCORR Procedure

• Example 30.1: Canonical Correlation Analysis of Fitness Club Data

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CANDISC Procedure

• Getting Started: CANDISC Procedure

• Example 31.1: Analyzing Iris Data by Using PROC CANDISC

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The CATMOD Procedure

• Weighted Least Squares Analysis of Mean Response
• Generalized Logits Model

• Example 32.1: Linear Response Function, r=2 Responses
• Example 32.2: Mean Score Response Function, r=3 Responses
• Example 32.3: Logistic Regression, Standard Response Function
• Example 32.4: Log-Linear Model, Three Dependent Variables
• Example 32.5: Log-Linear Model, Structural and Sampling Zeros
• Example 32.6: Repeated Measures, 2 Response Levels, 3 Populations
• Example 32.7: Repeated Measures, 4 Response Levels, 1 Population
• Example 32.8: Repeated Measures, Logistic Analysis of Growth Curve
• Example 32.9: Repeated Measures, Two Repeated Measurement Factors
• Example 32.10: Direct Input of Response Functions and Covariance Matrix
• Example 32.11: Predicted Probabilities

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CLUSTER Procedure

• Getting Started: CLUSTER Procedure

• Example 33.1: Cluster Analysis of Flying Mileages between 10 American Cities
• Example 33.2: Crude Birth and Death Rates
• Example 33.3: Cluster Analysis of Fisher’s Iris Data
• Example 33.4: Evaluating the Effects of Ties

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CORRESP Procedure

• Getting Started: CORRESP Procedure

• Example 34.1: Simple and Multiple Correspondence Analysis of Automobiles and Their Owners
• Example 34.2: Simple Correspondence Analysis of U.S. Population

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DISCRIM Procedure

• Getting Started: DISCRIM Procedure

• Example 35.1: Univariate Density Estimates and Posterior Probabilities
• Example 35.2: Bivariate Density Estimates and Posterior Probabilities
• Example 35.3: Normal-Theory Discriminant Analysis of Iris Data
• Example 35.4: Linear Discriminant Analysis of Remote-Sensing Data on Crops

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DISTANCE Procedure

• Creating a Distance Matrix as Input for a Subsequent Cluster Analysis

• Example 36.1: Divorce Grounds – the Jaccard Coefficient
• Example 36.2: Financial Data – Stock Dividends

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FACTOR Procedure

• Getting Started: FACTOR Procedure

• Example 37.1: Principal Component Analysis
• Example 37.2: Principal Factor Analysis
• Example 37.3: Maximum Likelihood Factor Analysis
• Example 37.4: Using Confidence Intervals to Locate Salient Factor Loadings

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FASTCLUS Procedure

• Getting Started: FASTCLUS Procedure

• Example 38.1: Fisher’s Iris Data
• Example 38.2: Outliers

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FMM Procedure

• Mixture Modeling for Binomial Overdispersion:
• Modeling Zero-Inflation: Is it Better to Fish Poorly or Not to Have Fished at All?
• Looking for Multiple Modes: Are Galaxies Clustered?

• Example 39.1: Modeling Mixing Probabilities: All Mice Are Created Equal, but Some Are More Equal
• Example 39.2: The Usefulness of Custom Starting Values: When Do Cows Eat?
• Example 39.3: Enforcing Homogeneity Constraints: Count and Dispersion—It Is All Over!

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FREQ Procedure

• Frequency Tables and Statistics
• Agreement Study

• Example 40.1: Output Data Set of Frequencies
• Example 40.2: Frequency Dot Plots
• Example 40.3: Chi-Square Goodness-of-Fit Tests
• Example 40.4: Binomial Proportions
• Example 40.5: Analysis of a 2x2 Contingency Table
• Example 40.6: Output Data Set of Chi-Square Statistics
• Example 40.7: Cochran-Mantel-Haenszel Statistics
• Example 40.8: Cochran-Armitage Trend Test
• Example 40.9: Friedman’s Chi-Square Test
• Example 40.10: Cochran’s Q Test

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GAM Procedure

• Getting Started: GAM Procedure

• Example 41.1: Generalized Additive Model with Binary Data
• Example 41.2: Poisson Regression Analysis of Component Reliability
• Example 41.3: Comparing PROC GAM with PROC LOESS

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GENMOD Procedure

• Poisson Regression
• Bayesian Analysis of a Linear Regression Model
• Generalized Estimating Equations

• Example 42.1: Logistic Regression
• Example 42.2: Normal Regression, Log Link
• Example 42.3: Gamma Distribution Applied to Life Data
• Example 42.4: Ordinal Model for Multinomial Data
• Example 42.5: GEE for Binary Data with Logit Link Function
• Example 42.6: Log Odds Ratios and the ALR Algorithm
• Example 42.7: Log-Linear Model for Count Data
• Example 42.8: Model Assessment of Multiple Regression Using Aggregates of Residuals
• Example 42.9: Assessment of a Marginal Model for Dependent Data
• Example 42.10: Bayesian Analysis of a Poisson Regression Model
• Example 42.11: Exact Poisson Regression
• Example 42.12: Tweedie Regression New!

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GLIMMIX Procedure

• Logistic Regressions with Random Intercepts

• Example 43.1: Binomial Counts in Randomized Blocks
• Example 43.2: Mating Experiment with Crossed Random Effects
• Example 43.3: Smoothing Disease Rates; Standardized Mortality Ratios
• Example 43.4: Quasi-likelihood Estimation for Proportions with Unknown Distribution
• Example 43.5: Joint Modeling of Binary and Count Data
• Example 43.6: Radial Smoothing of Repeated Measures Data
• Example 43.7: Isotonic Contrasts for Ordered Alternatives
• Example 43.8: Adjusted Covariance Matrices of Fixed Effects
• Example 43.9: Testing Equality of Covariance and Correlation Matrices
• Example 43.10: Multiple Trends Correspond to Multiple Extrema in Profile Likelihoods
• Example 43.11: Maximum Likelihood in Proportional Odds Model with Random Effects
• Example 43.12: Fitting a Marginal (GEE-Type) Model
• Example 43.13: Response Surface Comparisons with Multiplicity Adjustments
• Example 43.14: Generalized Poisson Mixed Model for Overdispersed Count Data
• Example 43.15: Comparing Multiple B-Splines
• Example 43.16: Diallel Experiment with Multimember Random Effects
• Example 43.17: Linear Inference Based on Summary Data
• Example 43.18: Weighted Multilevel Model for Survey Data New!

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GLM Procedure

• Simple Two-Way ANOVA
• Incorporating Contrasts, Unbalanced Designs, and Multiple Means Scenarios

• Example 46.1: One-Way ANOVA
• Example 46.2: Two-Way ANOVA with Covariate
• Example 46.3: Repeated Measures ANOVA New!

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GLMMOD Procedure

• A One-Way Design

• Example 45.1: A Two-Way Design
• Example 45.2: Factorial Screening

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GLMPOWER Procedure

• Simple Two-Way ANOVA
• Incorporating Contrasts, Unbalanced Designs, and Multiple Means Scenarios

• Example 46.1: One-Way ANOVA
• Example 46.2: Two-Way ANOVA with Covariate
• Example 46.3: Repeated Measures ANOVA

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GLMSELECT Procedure

• Getting Started: GLMSELECT Procedure

• Example 47.1: Modeling Baseball Salaries Using Performance Statistics
• Example 47.2: Using Validation and Cross Validation
• Example 47.3: Scatter Plot Smoothing by Selecting Spline Functions
• Example 47.4: Multimember Effects and the Design Matrix
• Example 47.5: Model Averaging
• Example 47.6: Elastic Net and External Cross Validation

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HPMIXED Procedure

• Mixed Model with Large Number of Fixed and Random Effects

• Example 48.1: Ranking Many Random-Effect Coefficients
• Example 48.2: Comparing Results from PROC HPMIXED and PROC MIXED
• Example 48.3: Using PROC GLIMMIX for Further Analysis of PROC HPMIXED Fit
• Example 48.4: Mixed Model Analysis of Microarray Data
• Example 48.5: Repeated Measures

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ICLIFETEST Procedure New Procedure!

• Getting Started: ICLIFETEST Procedure

• Example 49.1: Analyzing Data With Observations below a Limit of Detection
• Example 49.2: Controlling the Plotting of Survival Estimates
• Example 49.3: Plotting Kernel-Smoothed Hazard Functions

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INBREED Procedure

• The Format of the Input Data Set
• Performing the Analysis

• Example 50.1: Monoecious Population Analysis
• Example 50.2: Pedigree Analysis
• Example 50.3: Pedigree Analysis with BY Groups

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IRT Procedure New Procedure!

• A Simple 2PL Model

• Example 51.1: Unidimensional IRT Models
• Example 51.2: Multidimensional Exploratory and Confirmatory IRT Models
• Example 51.3: Multiple-Group Analysis

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KDE Procedure

• Getting Started: KDE Procedure

• Example 52.1: Computing a Basic Kernel Density Estimate
• Example 52.2: Changing the Bandwidth
• Example 52.3: Changing the Bandwidth (Bivariate)
• Example 52.4: Requesting Additional Output Tables
• Example 52.5: Univariate KDE Graphics
• Example 52.6: Bivariate KDE Graphics

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KRIGE2D Procedure

• Spatial Prediction Using Kriging, Contour Plots

• Example 53.1: Spatial Prediction of Pollutant Concentration
• Example 53.2: Investigating the Effect of Model Specification on Spatial Prediction
• Example 53.3: Data Quality and Prediction with Missing Values

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LATTICE Procedure

• Getting Started: LATTICE Procedure

• Example 54.1: Analysis of Variance through PROC LATTICE

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LIFEREG Procedure

• Modeling Right-Censored Failure Time Data
• Bayesian Analysis of Right-Censored Data

• Example 55.1: Motorette Failure
• Example 55.2: Computing Predicted Values for a Tobit Model
• Example 55.3: Overcoming Convergence Problems by Specifying Initial Values
• Example 55.4: Analysis of Arbitrarily Censored Data with Interaction Effects
• Example 55.5: Probability Plotting—Right Censoring
• Example 55.6: Probability Plotting—Arbitrary Censoring
• Example 55.7: Bayesian Analysis of Clinical Trial Data
• Example 55.8: Model Postfitting Analysis

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LIFETEST Procedure

• Getting Started: LIFETEST Procedure

• Example 56.1: Product-Limit Estimates and Tests of Association
• Example 56.2: Enhanced Survival Plot and Multiple-Comparison Adjustments
• Example 56.3: Life-Table Estimates for Males with Angina Pectoris

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LOESS Procedure

• Scatter Plot Smoothing

• Example 57.1: Engine Exhaust Emissions
• Example 57.2: Sulfate Deposits in the U.S. for 1990
• Example 57.3: Catalyst Experiment
• Example 57.4: El Niño Southern Oscillation

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LOGISTIC Procedure

• Getting Started: LOGISTIC Procedure

• Example 58.1: Stepwise Logistic Regression and Predicted Values
• Example 58.2: Logistic Modeling with Categorical Predictors
• Example 58.3: Ordinal Logistic Regression
• Example 58.4: Nominal Response Data: Generalized Logits Model
• Example 58.5: Stratified Sampling
• Example 58.6: Logistic Regression Diagnostics
• Example 58.7: ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
• Example 58.8: Comparing Receiver Operating Characteristic Curves
• Example 58.9: Goodness-of-Fit Tests and Subpopulations
• Example 58.10: Overdispersion
• Example 58.11: Conditional Logistic Regression for Matched Pairs Data
• Example 58.12: Firth’s Penalized Likelihood Compared with Other Approaches
• Example 58.13: Complementary Log-Log Model for Infection Rates
• Example 58.14: Complementary Log-Log Model for Interval-Censored Survival Times
• Example 58.15: Scoring Data Sets
• Example 58.16: Using the LSMEANS Statement
• Example 58.17: Partial Proportional Odds Model

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MCMC Procedure

• Simple Linear Regression
• The Behrens-Fisher Problem
• Random-Effects Model

• Example 59.1: Simulating Samples From a Known Density
• Example 59.2: Box-Cox Transformation
• Example 59.3: Logistic Regression Model with a Diffuse Prior
• Example 59.4: Logistic Regression Model with Jeffreys’ Prior
• Example 59.5: Poisson Regression
• Example 59.6: Nonlinear Poisson Regression Models
• Example 59.7: Logistic Regression Random-Effects Model
• Example 59.8: Nonlinear Poisson Regression Multilevel Random-Effects Model
• Example 59.9: Multivariate Normal Random-Effects Model
• Example 59.10: Missing at Random Analysis
• Example 59.11: Nonignorably Missing Data (MNAR) Analysis
• Example 59.12: Change Point Models
• Example 59.13: Exponential and Weibull Survival Analysis
• Example 59.14: Time Independent Cox Model
• Example 59.15: Time Dependent Cox Model
• Example 59.16: Piecewise Exponential Frailty Model
• Example 59.17: Normal Regression with Interval Censoring
• Example 59.18: Constrained Analysis
• Example 59.19: Implement a New Sampling Algorithm
• Example 59.20: Using a Transformation to Improve Mixing
• Example 59.21: Gelman-Rubin Diagnostics

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MDS Procedure

• Getting Started: MDS Procedure

• Example 60.1: Jacobowitz Body Parts Data from Children and Adults

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MI Procedure

• Getting Started: MI Procedure

• Example 61.1: EM Algorithm for MLE
• Example 61.2: Monotone Propensity Score Method
• Example 61.3: Monotone Regression Method
• Example 61.4: Monotone Logistic Regression Method for CLASS Variables
• Example 61.5: Monotone Discriminant Function Method for CLASS Variables
• Example 61.6: FCS Method for Continuous Variables
• Example 61.7: FCS Method for CLASS Variables
• Example 61.8: FCS Method with Trace Plot
• Example 61.9: MCMC Method
• Example 61.10: Producing Monotone Missingness with MCMC
• Example 61.11: Checking Convergence in MCMC
• Example 61.12: Saving and Using Parameters for MCMC
• Example 61.13: Transforming to Normality
• Example 61.14: Multistage Imputation
• Example 61.15: Creating Control-Based Pattern Imputation in Sensitivity Analysis New!
• Example 61.16: Adjusting Imputed Continuous Values in Sensitivity Analysis New!
• Example 61.17: Adjusting Imputed Classification Levels in Sensitivity Analysis New!
• Example 61.18: Adjusting Imputed Values with Parameters in a Data Set New!

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MIANALYZE Procedure

• Getting Started: MIANALYZE Procedure

• Example 62.1: Reading Means and Standard Errors from a DATA= Data Set
• Example 62.2: Reading Means and Covariance Matrices from a DATA= COV Data Set
• Example 62.3: Reading Regression Results from a DATA= EST Data Set
• Example 62.4: Reading Mixed Model Results from PARMS= and COVB= Data Sets
• Example 62.5: Reading Generalized Linear Model Results
• Example 62.6: Reading GLM Results from PARMS= and XPXI= Data Sets
• Example 62.7: Reading Logistic Model Results from a PARMS= Data Set
• Example 62.8: Reading Mixed Model Results with Classification Covariates
• Example 62.9: Reading Nominal Logistic Model Results
• Example 62.10: Using a TEST statement
• Example 62.11: Combining Correlation Coefficients
• Example 62.12: Sensitivity Analysis with Control-Based Pattern Imputation
• Example 62.13: Sensitivity Analysis with Tipping-Point Approach

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MIXED Procedure

• Clustered Data Example

• Example 63.1: Split-Plot Design
• Example 63.2: Repeated Measures
• Example 63.3: Plotting the Likelihood
• Example 63.4: Known G and R
• Example 63.5: Random Coefficients
• Example 63.6: Line-Source Sprinkler Irrigation
• Example 63.7: Influence in Heterogeneous Variance Model
• Example 63.8: Influence Analysis for Repeated Measures Data
• Example 63.9: Examining Individual Test Components
• Example 63.10: Isotonic Contrasts for Ordered Mean Values

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MODECLUS Procedure

• Getting Started: MODECLUS Procedure

• Example 64.1: Cluster Analysis of Samples from Univariate Distributions
• Example 64.2: Cluster Analysis of Flying Mileages between Ten American Cities
• Example 64.3: Cluster Analysis with Significance Tests
• Example 64.4: Cluster Analysis: Hertzsprung-Russell Plot
• Example 64.5: Using the TRACE Option When METHOD=6

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MULTTEST Procedure

• Drug Example

• Example 65.1: Cochran-Armitage Test with Permutation Resampling
• Example 65.2: Freeman-Tukey and
• Example 65.3: Peto Mortality-Prevalence Test
• Example 65.4: Fisher Test with Permutation Resampling
• Example 65.5: Inputting Raw
• Example 65.6: Adaptive Adjustments and ODS Graphics

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NESTED Procedure

• Reliability of Automobile Models

• Example 66.1: Variability of Calcium Concentration in Turnip Greens

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NLIN Procedure

• Nonlinear or Linear Model
• Notation for Nonlinear Regression Models
• Estimating the Parameters in the Nonlinear Model

• Example 67.1: Segmented Model
• Example 67.2: Iteratively Reweighted Least Squares
• Example 67.3: Probit Model with Likelihood Function
• Example 67.4: Affecting Curvature through Parameterization
• Example 67.5: Comparing Nonlinear Trends among Groups
• Example 67.6: ODS Graphics and Diagnostics
• Example 67.7: Parameter Profiling and Bootstrapping New!

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NLMIXED Procedure

• Nonlinear Growth Curves with Gaussian Data
• Logistic-Normal Model with Binomial Data

• Example 68.1: One-Compartment Model with Pharmacokinetic Data
• Example 68.2: Probit-Normal Model with Binomial Data
• Example 68.3: Probit-Normal Model with Ordinal Data
• Example 68.4: Poisson-Normal Model with Count Data
• Example 68.5: Failure Time and Frailty Model

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NPAR1WAY Procedure

• Getting Started: NPAR1WAY Procedure

• Example 69.1: Two-Sample Location Tests and Plots
• Example 69.2: EDF Statistics and EDF Plot
• Example 69.3: Exact Wilcoxon Two-Sample Test
• Example 69.4: Hodges-Lehmann Estimation
• Example 69.5: Exact Savage Multisample Test

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ORTHOREG Procedure

• Longley Data

• Example 70.1: Precise Analysis of Variance
• Example 70.2: Wampler Data
• Example 70.3: Fitting Polynomials

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PHREG Procedure

• Classical Method of Maximum Likelihood
• Bayesian Analysis

• Example 71.1: Stepwise Regression
• Example 71.2: Best Subset Selection
• Example 71.3: Modeling with Categorical Predictors
• Example 71.4: Firth’s Correction for Monotone Likelihood
• Example 71.5: Conditional Logistic Regression for m:n Matching
• Example 71.6: Model Using Time-Dependent Explanatory Variables
• Example 71.7: Time-Dependent Repeated Measurements of a Covariate
• Example 71.8: Survival Curves
• Example 71.9: Analysis of Residuals
• Example 71.10: Analysis of Recurrent Events Data
• Example 71.11: Analysis of Clustered Data
• Example 71.12: Model Assessment Using Cumulative Sums of Martingale Residuals
• Example 71.13: Bayesian Analysis of the Cox Model
• Example 71.14: Bayesian Analysis of Piecewise Exponential Model
• Example 71.15: Analysis of Competing-Risks Data New!

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PLAN Procedure

• Three Replications with Four Factors
• Randomly Assigning Subjects to Treatments

• Example 72.1: A Split-Plot Design
• Example 72.2: A Hierarchical Design
• Example 72.3: An Incomplete Block Design
• Example 72.4: A Latin Square Design
• Example 72.5: A Generalized Cyclic Incomplete Block Design
• Example 72.6: Permutations and Combinations
• Example 72.7: Crossover Designs

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PLM Procedure

• Getting Started: PLM Procedure

• Example 73.1: Scoring with PROC PLM
• Example 73.2: Working with Item Stores
• Example 73.3: Group Comparisons in an Ordinal Model
• Example 73.4: Posterior Inference for Binomial Data
• Example 73.5: BY-Group Processing
• Example 73.6: Comparing Multiple B-Splines
• Example 73.7: Linear Inference with Arbitrary Estimates

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PLS Procedure

• Spectrometric Calibration

• Example 74.1: Examining Model Details
• Example 74.2: Examining Outliers
• Example 74.3: Choosing a PLS Model by Test Set Validation
• Example 74.4: Partial Least Squares Spline Smoothing

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POWER Procedure

• Computing Power for a One-Sample
• Determining Required Sample Size for a Two-Sample

• Example 75.1: One-Way ANOVA
• Example 75.2: The Sawtooth Power Function in Proportion Analyses
• Example 75.3: Simple AB/BA Crossover Designs
• Example 75.4: Noninferiority Test with Lognormal Data
• Example 75.5: Multiple Regression and Correlation
• Example 75.6: Comparing Two Survival Curves
• Example 75.7: Confidence Interval Precision
• Example 75.8: Customizing Plots
• Example 75.9: Binary Logistic Regression with Independent Predictors
• Example 75.10: Wilcoxon-Mann-Whitney Test

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PRINCOMP Procedure

• Getting Started: PRINCOMP Procedure

• Example 77.1: Analyzing Mean Temperatures of US Cities
• Example 77.2: Analyzing Rankings of US College Basketball Teams
• Example 77.3: Analyzing Job Ratings of Police Officers

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PRINQUAL Procedure

• Getting Started: PRINQUAL Procedure

• Example 78.1: Multidimensional Preference Analysis of Automobile Data
• Example 78.2: Principal Components of Basketball Rankings

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PROBIT Procedure

• Estimating the Natural Response Threshold Parameter

• Example 79.1: Dosage Levels
• Example 79.2: Multilevel Response
• Example 79.3: Logistic Regression
• Example 79.4: An Epidemiology Study
• Example 79.5: Model Postfitting Analysis

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QUANTLIFE Procedure

• Getting Started: QUANTLIFE Procedure

• Example 80.1: Primary Biliary Cirrhosis Study
• Example 80.2: Drug Abuse Study

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QUANTREG Procedure

• Analysis of Fish-Habitat Relationships
• Growth Charts for Body Mass Index

• Example 81.1: Comparison of Algorithms
• Example 81.2: Quantile Regression for Econometric Growth Data
• Example 81.3: Quantile Regression Analysis of Birth-Weight Data
• Example 81.4: Nonparametric Quantile Regression for Ozone Levels
• Example 81.5: Quantile Polynomial Regression for Salary Data

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QUANTSELECT Procedure

• Getting Started: QUANTSELECT Procedure

• Example 82.1: Simulation Study
• Example 82.2: Econometric Growth Data
• Example 82.3: Pollution and Mortality

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REG Procedure

• Simple Linear Regression
• Polynomial Regression
• Using PROC REG Interactively

• Example 83.1: Modeling Salaries of Major League Baseball Players
• Example 83.2: Aerobic Fitness Prediction
• Example 83.3: Predicting Weight by Height and Age
• Example 83.4: Regression with Quantitative and Qualitative Variables
• Example 83.5: Ridge Regression for Acetylene Data
• Example 83.6: Chemical Reaction Response

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ROBUSTREG Procedure

• M Estimation
• LTS Estimation

• Example 84.1: Comparison of Robust Estimates
• Example 84.2: Robust ANOVA
• Example 84.3: Growth Study of De Long and Summers
• Example 84.4: Constructed Effects
• Example 84.5: Robust Diagnostics

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RSREG Procedure

• A Response Surface with a Simple Optimum

• Example 85.1: A Saddle Surface Response Using Ridge Analysis
• Example 85.2: Response Surface Analysis with Covariates

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SEQDESIGN Procedure

• Getting Started: SEQDESIGN Procedure

• Example 87.1: Creating Fixed-Sample Designs
• Example 87.2: Creating a One-Sided O’Brien-Fleming Design
• Example 87.3: Creating Two-Sided Pocock and O’Brien-Fleming Designs
• Example 87.4: Generating Graphics Display for Sequential Designs
• Example 87.5: Creating Designs Using Haybittle-Peto Methods
• Example 87.6: Creating Designs with Various Stopping Criteria
• Example 87.7: Creating Whitehead’s Triangular Designs
• Example 87.8: Creating a One-Sided Error Spending Design
• Example 87.9: Creating Designs with Various Number of Stages
• Example 87.10: Creating Two-Sided Error Spending Designs with and without Overlapping Lower and Upper
• Example 87.11: Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject
• Example 87.12: Creating a Two-Sided Asymmetric Error Spending Design with Early Stopping to Reject or Accept
• Example 87.13: Creating a Design with a Nonbinding Beta Boundary
• Example 87.14: Computing Sample Size for Survival Data That Have Uniform Accrual
• Example 87.15: Computing Sample Size for Survival Data with Truncated Exponential Accrual

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SEQTEST Procedure

• Getting Started: SEQTEST Procedure

• Example 88.1: Testing the Difference between Two Proportions
• Example 88.2: Testing an Effect in a Regression Model
• Example 88.3: Testing an Effect with Early Stopping to Accept
• Example 88.4: Testing a Binomial Proportion
• Example 88.5: Comparing Two Proportions with a Log Odds Ratio Test
• Example 88.6: Comparing Two Survival Distributions with a Log-Rank Test
• Example 88.7: Testing an Effect in a Proportional Hazards Regression Model
• Example 88.8: Testing an Effect in a Logistic Regression Model
• Example 88.9: Conducting a Trial with a Nonbinding Acceptance Boundary

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SCORE Procedure

• Getting Started: SCORE Procedure

• Example 86.1: Factor Scoring Coefficients
• Example 86.2: Regression Parameter Estimates
• Example 86.3: Custom Scoring Coefficients

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SIM2D Procedure

• Preliminary Spatial Data Analysis
• Investigating Variability by Simulation

• Example 89.1: Simulation and Economic Feasibility
• Example 89.2: Variability at Selected Locations
• Example 89.3: Risk Analysis with Simulation

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SIMNORMAL Procedure

• Getting Started: SIMNORMAL Procedure

• Example : Example: SIMNORM Procedure

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STDIZE Procedure

• Getting Started: STDIZE Procedure

• Example 91.1: Standardization of Variables in Cluster Analysis

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STDRATE Procedure

• Getting Started: STDRATE Procedure

• Example 92.1: Comparing Directly Standardized Rates
• Example 92.2: Computing Mantel-Haenszel Risk Estimation
• Example 92.3: Computing Attributable Fraction Estimates
• Example 92.4: Displaying SMR Results from BY Groups

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STEPDISC Procedure

• Getting Started: STEPDISC Procedure

• Example 93.1: Performing a Stepwise Discriminant Analysis

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SURVEYFREQ Procedure

• Getting Started: SURVEYFREQ Procedure

• Example 94.1: Two-Way Tables
• Example 94.2: Multiway Tables (Domain Analysis)
• Example 94.3: Output Data Sets

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SURVEYLOGISTIC Procedure

• Getting Started: SURVEYLOGISTIC Procedure

• Example 95.1: Stratified Cluster Sampling
• Example 95.2: The Medical Expenditure Panel Survey (MEPS)

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SURVEYMEANS Procedure

• Simple Random Sampling
• Stratified Sampling
• Output Data Sets

• Example 96.1: Stratified Cluster Sample Design
• Example 96.2: Domain Analysis
• Example 96.3: Ratio Analysis
• Example 96.4: Analyzing Survey Data with Missing Values
• Example 96.5: Variance Estimation Using Replication Methods

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SURVEYPHREG Procedure

• Getting Started: SURVEYPHREG Procedure

• Example 97.1: Analysis of Clustered Data
• Example 97.2: Stratification, Clustering, and Unequal Weights
• Example 97.3: Domain Analysis
• Example 97.4: Variance Estimation by Using Replicate Weights
• Example 97.5: A Test of the Proportional Hazards Assumption by Using the Programming Statements

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SURVEYREG Procedure

• Simple Random Sampling
• Stratified Sampling
• Output Data Sets

• Example 98.1: Simple Random Sampling
• Example 98.2: Cluster Sampling
• Example 98.3: Regression Estimator for Simple Random Sample
• Example 98.4: Stratified Sampling
• Example 98.5: Regression Estimator for Stratified Sample
• Example 98.6: Stratum Collapse
• Example 98.7: Domain Analysis
• Example 98.8: Compare Domain Statistics
• Example 98.9: Variance Estimate Using the Jackknife Method

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SURVEYSELECT Procedure

• Simple Random Sampling
• Stratified Sampling
• Stratified Sampling with Control Sorting

• Example 99.1: Replicated Sampling
• Example 99.2: PPS Selection of Two Units per Stratum
• Example 99.3: PPS (Dollar-Unit) Sampling
• Example 99.4: Proportional Allocation

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TCALIS Procedure

• Path Analysis: Stability of Alienation
• Simultaneous Equations with Mean Structures and Reciprocal Paths
• A Direct Covariance Structures Model
• Confirmatory Factor Analysis: Cognitive Abilities
• Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis
• Illustrating Various General Modeling Languages
• Fitting a Latent Growth Curve Model
• Higher-Order and Hierarchical Factor Models
• Linear Relations among Factor Loadings
• A Multiple-Group Model for Purchasing Behavior

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TPSPLINE Procedure

• Getting Started: TPSPLINE Procedure

• Example 100.1: Partial Spline Model Fit
• Example 100.2: Spline Model with Higher-Order Penalty
• Example 100.3: Multiple Minima of the GCV Function
• Example 100.4: Large Data Set Application
• Example 100.5: Computing a Bootstrap Confidence Interval

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TRANSREG Procedure

• Fitting a Curve through a Scatter Plot
• Main-Effects ANOVA

• Example 101.1: Transformation Regression of Exhaust Emissions Data
• Example 101.2: Box-Cox Transformations
• Example 101.3: Penalized B-Spline
• Example 101.4: Nonmetric Conjoint Analysis of Tire Data
• Example 101.5: Metric Conjoint Analysis of Tire Data
• Example 101.6: Preference Mapping of Automobile Data

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TREE Procedure

• Getting Started: TREE Procedure

• Example 102.1: Mammals’ Teeth
• Example 102.2: Iris Data

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TTEST Procedure

• One-Sample
• Comparing Group Means

• Example 103.1: Using Summary Statistics to Compare Group Means
• Example 103.2: One-Sample Comparison with the FREQ Statement
• Example 103.3: Paired Comparisons
• Example 103.4: AB/BA Crossover Design
• Example 103.5: Equivalence Testing with Lognormal Data

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VARCLUS Procedure

• Getting Started: VARCLUS Procedure

• Example 104.1: Correlations among Physical Variables

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VARCOMP Procedure

• Analyzing the Cure Rate of Rubber

• Example 105.1: Using the Four General Estimation Methods
• Example 105.2: Using the GRR Method

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VARIOGRAM Procedure

• Preliminary Spatial Data Analysis
• Empirical Semivariogram Computation
• Autocorrelation Analysis
• Theoretical Semivariogram Model Fitting

• Example 106.1: Aspects of Semivariogram Model Fitting
• Example 106.2: An Anisotropic Case Study with Surface Trend in the Data
• Example 106.3: Analysis without Surface Trend Removal
• Example 106.4: Covariogram and Semivariogram
• Example 106.5: A Box Plot of the Square Root Difference Cloud

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