Overview

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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.

Syllabus

Foundations of Regression Analysis

This module introduces learners to the fundamentals of regression and lays the groundwork for understanding logistic regression. Beginning with an overview of regression analysis, the module explores how dependent and independent variables interact, the role of coefficients, and the importance of error terms in modeling. Learners will examine practical applications of regression in real-world problem-solving and policymaking. The module then transitions into different probability prediction methods, highlighting when and why logistic regression is more appropriate than ordinary least squares (OLS). By the end, learners will have a solid conceptual foundation for applying logistic regression to binary outcomes in analytics and decision-making contexts.

Logistic Regression in Practice

This module deepens the learner’s understanding of logistic regression by focusing on key modeling concepts, practical approaches, and industry-standard methodologies. Learners will explore foundational ideas such as observation periods, validation samples, and outlier treatment before moving into logistic regression’s core principles, including the logit transformation, odds-to-probability conversion, and the importance of Maximum Likelihood Estimation (MLE). The module introduces practical approaches such as the Binning, Continuous, and Dummy Variable methods to improve model stability. Learners will then engage with SAS-based methodologies for variable selection, PROC LOGISTIC procedures, and evaluation techniques such as concordant/discordant pairs and global vs local measures of model fit. By the end of this module, learners will have the applied knowledge to build, evaluate, and refine logistic regression models in real-world data science and analytics contexts.