SPSS: Apply & Interpret Logistic Regression Models

Build practical skills in logistic regression and supervised learning using IBM SPSS Statistics through a hands-on, application-focused learning experience. This course introduces the foundations of logistic regression while guiding you through the complete process of preparing data, configuring variables, building predictive models, and interpreting statistical outputs in SPSS. You will learn how to navigate the SPSS environment, apply logistic regression techniques, and analyze model results using structured datasets. Through guided case studies, including heart pulse analysis and smoking behavior classification, you will construct logistic regression equations, evaluate predictor significance, assess model performance, and interpret statistical evidence to support data-driven decisions. The course also reinforces key concepts with Excel-based logistic modeling. Designed for learners who want practical experience with predictive analytics in SPSS, this course combines conceptual understanding with step-by-step implementation. By the end of the course, you will be able to develop logistic regression models, interpret SPSS output tables, evaluate prediction accuracy, and confidently communicate analytical findings using statistical evidence.