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

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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.

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.

More Complex Linear Models

• Performing two-way ANOVA with and without interactions.
• Understanding the concepts of multiple regression.

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.

Model Post-Fitting for Inference

• Examining residuals.
• Investigating influential observations.
• Assessing collinearity.

Model Building and Scoring for Prediction

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

Categorical Data Analysis

• Producing frequency tables with the FREQ procedure.
• Examining tests for general and linear association using the FREQ procedure.
• Understanding exact tests.
• Understanding 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.