This module introduces learners to the foundations of logistic regression and the importance of data preparation when working in SAS. Students explore the basics of binary classification, apply logistic regression using PROC LOGISTIC, and prepare datasets by handling missing values and encoding categorical variables. By the end of this module, learners will have the skills to structure datasets correctly and build their first logistic regression models in SAS.

This module focuses on advanced data preparation techniques to improve logistic regression performance. Learners examine variable clustering to reduce redundancy, use screening techniques to evaluate predictor importance, and explore subset selection methods to refine model inputs. The emphasis is on selecting the most relevant predictors, improving efficiency, and ensuring model stability in SAS.

This module advances into model building strategies and performance evaluation. Students explore stepwise and backward elimination techniques to refine predictors, implement models using PROC LOGISTIC and ODS, and assess model performance with misclassification analysis, confusion matrices, and logit plots. Learners will gain the ability to build robust logistic regression models and validate them effectively in SAS.