Predictive Modeling and Logistic Regression in SAS

Organizations rely on data-driven predictions to anticipate customer behavior, assess risk, and optimize operations. Understanding how to build and interpret binary classification models is a core skill for any aspiring analyst or data professional. This written course guides you from the absolute basics of statistical modeling to constructing, validating, and interpreting logistic regression models using SAS. You will learn how to prepare your data, select the right predictors, and translate complex statistical outputs into actionable business insights. Through structured written lessons and code-based exercises, you will develop the practical skills needed to solve real-world classification problems. What you'll learn: - Understand the fundamental concepts of binary logistic regression and predictive modeling. - Prepare raw data by handling missing values and recoding categorical variables using modern weight of evidence techniques. - Select optimal predictive variables using automated selection procedures and interaction analysis. - Evaluate model performance using key metrics like ROC curves, confusion matrices, and lift charts. - Interpret odds ratios and parameter estimates to explain model predictions to business stakeholders. - Apply efficiency techniques to scale your predictive models for massive datasets. The course begins with foundational statistical definitions and data preparation workflows before moving step-by-step through model building, validation, and practical interpretation. You will progress through clear written explanations and structured coding exercises designed to build practical confidence. This course is designed for beginners, data analysts, and aspiring risk modelers, with no prior predictive modeling experience required. Start reading today to master the fundamentals of predictive analytics using SAS.