Gain a strong foundation in logistic regression and learn how to build, interpret, and evaluate predictive models for binary outcomes. This beginner-friendly course introduces the core principles of regression analysis before guiding you through the concepts and practical techniques that make logistic regression one of the most widely used methods in data science, predictive analytics, and business analytics.

You will begin by exploring regression fundamentals, including dependent and independent variables, coefficients, and error terms. Next, you will compare probability prediction methods and understand why logistic regression is preferred over ordinary least squares (OLS) for binary classification problems. As you progress, you will analyze logit transformation, odds and probability interpretation, and Maximum Likelihood Estimation (MLE). You will also explore binning, continuous, and dummy variable approaches to improve model stability, apply SAS methodologies with PROC LOGISTIC, and evaluate models using concordant and discordant pairs, chi-square tests, and global versus local goodness-of-fit measures. Designed for beginners, aspiring data analysts, analytics professionals, and learners interested in predictive modeling, this course combines statistical foundations with practical model evaluation techniques to help you confidently analyze and assess logistic regression models for data-driven decision-making.