Course structure
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.
