Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables

Please contact us for information about prerequisites.

Expected Duration
3 day


This course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t-tests, ANOVA, linear regression, and logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum.


  • SAS Certified Clinical Trials Programmer Using SAS 9
  • SAS Statistical Business Analysis Using SAS 9: Regression and Modeling


1. Course Overview and Review of Concepts

  • Descriptive statistics
  • Inferential statistics
  • Examining data distributions
  • Obtaining and interpreting sample statistics using the univariate procedure
  • Examining data distributions graphically in the univariate and freq procedures
  • Constructing confidence intervals
  • Performing simple tests of hypothesis
  • Performing tests of differences between two group means using PROC TTEST

2. ANOVA and Regression

  • Performing one-way ANOVA with the GLM procedure
  • Performing post-hoc multiple comparisons tests in PROC GLM
  • Producing correlations with the CORR procedure
  • Fitting a simple linear regression model with the REG procedure

3. More Complex Linear Models

  • Performing two-way ANOVA with and without interactions
  • The concepts of multiple regression

4. Model Building and Effect Selection

  • Automated model selection techniques in PROC GLMSELECT to choose from among several candidate models
  • Interpreting and comparison of selected models

5. Model Post-Fitting for Inference

  • Examining residuals
  • Investigating influential observations
  • Assessing collinearity

6. Model Building and Scoring for Prediction

  • The concepts of predictive modeling
  • The importance of data partitioning
  • The concepts of scoring
  • Obtaining predictions (scoring) for new data using PROC GLMSELECT and PROC PLM

7. Categorical Data Analysis

  • Producing frequency tables with the FREQ procedure
  • Examining tests for general and linear association using the FREQ procedure
  • Exact tests
  • The concepts of logistic regression
  • Fitting univariate and multivariate logistic regression models using the LOGISTIC procedure
  • Using automated model selection techniques in PROC LOGISTIC including interaction terms
  • Obtaining predictions (scoring) for new data using PROC PLM



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