Logistic Regression in SAS: Model Building and Evaluation

Understanding categorical outcomes is a cornerstone of modern predictive analytics. This text-based course guides you through the foundational concepts of logistic regression and shows you how to implement, evaluate, and interpret these models using SAS. You will transition from understanding basic statistical concepts to confidently preparing datasets, coding regression procedures, and evaluating model performance using industry-standard metrics. What you'll learn: 1. Understand the mathematical foundations of logistic regression, odds ratios, and binary outcomes. 2. Prepare and clean datasets specifically for categorical modeling in SAS. 3. Execute logistic regression workflows using SAS procedures like PROC LOGISTIC. 4. Evaluate model fit and predictive power using ROC curves, AIC, and confusion matrices. 5. Interpret SAS output to make actionable, data-driven business recommendations. 6. Address common modeling challenges such as multicollinearity and imbalanced data. The course begins with essential statistical terminology and theoretical foundations before guiding you through step-by-step SAS code examples, data preparation workflows, and model diagnostic interpretations. This course is designed for beginners, data analysts, and aspiring data scientists with no prior SAS experience or advanced statistical background required. Start reading today to master logistic regression in SAS and elevate your data analysis skills.