This beginner-friendly course provides a comprehensive introduction to logistic regression, one of the most widely used techniques in data science and analytics. Learners will explain regression fundamentals, differentiate probability prediction methods, and analyze logistic regression key concepts including logit transformation, odds interpretation, and Maximum Likelihood Estimation (MLE).

The course progresses from foundational regression principles to practical applications of logistic regression, covering approaches such as binning, continuous, and dummy variable transformations. Learners will also apply SAS methodologies for variable selection, use PROC LOGISTIC, and evaluate model performance with concordant/discordant pairs, chi-square tests, and global vs local goodness-of-fit measures. By the end of the course, participants will be able to design stable predictive models, interpret results with confidence, and evaluate logistic regression models for real-world decision-making in analytics and business intelligence.