Logistic Regression with SAS: Build & Evaluate Models

Overview

By completing this course, learners will be able to implement logistic regression models in SAS, prepare datasets through missing value imputation and categorical encoding, analyze predictors using clustering and screening, and evaluate models with confusion matrices and logit plots. Designed for aspiring data scientists, analysts, and business professionals, this course blends statistical rigor with hands-on SAS demonstrations. Learners will benefit by gaining both technical knowledge and practical skills to solve real-world classification problems, such as predicting customer behavior, assessing risk, or identifying fraud. Unlike generic statistical tutorials, this course uniquely emphasizes feature engineering, subset selection, and SAS-specific implementation to ensure models are not only accurate but also interpretable and business-ready. Through structured modules, learners progress from foundational concepts to advanced evaluation, ensuring they can confidently build, optimize, and validate logistic regression models. By the end, participants will have mastered the end-to-end workflow of logistic regression in SAS, positioning themselves for success in data-driven roles across industries.

Syllabus

Logistic Regression Foundations and Data Setup

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.

Feature Engineering and Predictor Selection

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.

Model Building and Performance Evaluation

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.