Logistic Regression with SAS: Build & Evaluate Models

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.