Master Logistic Regression with SAS: Build & Evaluate Models is a hands-on course that teaches you how to develop, refine, and evaluate logistic regression models using SAS. You will begin by exploring the role of logistic regression in predictive modeling, working with real-world insurance data, and implementing your first models with PROC LOGISTIC. As you progress, you will prepare datasets by handling missing values, encoding categorical variables, and applying data preparation techniques that support reliable model performance.

Designed for aspiring data scientists, data analysts, and business professionals, this course provides a structured learning path from foundational concepts to advanced model optimization. You will learn how to reduce predictor redundancy through variable clustering, evaluate predictor importance using statistical screening methods, and apply subset selection techniques to identify the most effective model inputs. In the final module, you will refine logistic regression models using stepwise and backward elimination, implement models with PROC LOGISTIC and ODS, and evaluate predictive performance using misclassification analysis, confusion matrices, and logit plots. Throughout the course, you will gain practical SAS experience while learning an end-to-end workflow for building interpretable, well-validated classification models. If you want to strengthen your predictive modeling skills and confidently apply logistic regression in SAS, this course provides a practical, project-focused learning experience.