SPSS: Apply & Interpret Logistic Regression Models

Overview

This course provides a practical and applied introduction to logistic regression and supervised learning using IBM SPSS Statistics. Designed for learners seeking to build analytical skills in predictive modeling, the course emphasizes both conceptual understanding and tool-based execution. Through step-by-step instruction, learners will identify key components of logistic regression, configure data within SPSS, and construct predictive models using real-world case studies. They will analyze model outputs, evaluate predictor significance, and interpret statistical results to make informed decisions. The course integrates Excel-based logistic modeling and reinforces learning through guided examples such as heart pulse analysis and smoking behavior classification. By the end, learners will be able to confidently apply logistic regression methods to structured datasets, assess model performance using statistical evidence, and communicate findings through SPSS-generated outputs.

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.