Overview

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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.

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.