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SPSS: Apply & Interpret Logistic Regression Models

EDUCBA via Coursera

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Overview

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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.

Syllabus

Logistic Regression & Supervised Learning using SPSS

This module introduces learners to the foundational principles of logistic regression and equips them with hands-on skills in SPSS for managing variables, configuring the data environment, and interpreting statistical outputs. Learners will explore both theoretical concepts and practical applications, including variable setup, SPSS navigation, model output interpretation, and foundational logistic modeling techniques. By the end of this module, learners will be capable of preparing, analyzing, and interpreting logistic regression models using SPSS and supporting tools like MS Excel.

Applied Analysis and Output Interpretation

This module focuses on applying logistic regression techniques through real-world case studies and interpreting model results in SPSS. Learners will work with datasets such as heart pulse and smoking behavior, construct logistic equations, analyze variable significance, and interpret model output to draw actionable conclusions. The module emphasizes evaluating model performance and improving prediction accuracy using statistical evidence from SPSS output tables.