Predictive Analytics with SPSS: Analyze & Apply

Overview

By the end of this course, learners will be able to import and manage datasets in SPSS, apply descriptive statistics, analyze correlations, construct linear and multiple regression models, and interpret logistic and multinomial regression outputs. Through hands-on practice with real-world case studies—including heart pulse, copper expansion, energy consumption, and debt assessment—learners will evaluate predictors, interpret coefficients, and validate results. This course is designed to build a step-by-step mastery of predictive analytics using SPSS, starting from data handling fundamentals to advanced regression modeling. Each module integrates theory with applied case studies, enabling learners to connect statistical concepts to practical decision-making. What makes this course unique is its structured approach that combines clear explanations, SPSS demonstrations, and diverse datasets across health, psychology, and finance domains. Learners will gain not only technical proficiency in SPSS but also the confidence to apply predictive modeling techniques in real-world research, business, and academic contexts. Whether you are a student, researcher, or professional, this course equips you with the tools to transform raw data into actionable insights.

Syllabus

Importing Data and SPSS Fundamentals

This module introduces learners to importing data into SPSS, navigating software menus, and applying basic statistical concepts such as mean and standard deviation. Learners will also practice handling different data formats and explore essential data management tasks within SPSS.

Correlation and Initial Data Visualization

This module focuses on correlation analysis and data visualization techniques. Learners will explore scatter plots, SPSS data editor tools, and real-world case studies to understand relationships between variables.

Linear Regression Modeling

This module builds foundational knowledge of linear regression, from simple equations to real-world applications. Learners will study regression coefficients, interpret model outputs, and apply regression in diverse case studies such as copper expansion and energy consumption.

Multiple Regression Applications

This module covers multiple regression and its applications in financial and health datasets. Learners will refine regression models, calculate predicted values, and explore case studies involving debt assessment and credit card data.

Advanced Regression and Logistic Analysis

This module introduces advanced regression interpretation and logistic regression concepts. Learners will explore logistic regression case studies, define variables correctly in SPSS, and understand outputs such as coefficients and odds ratios.

Multinomial Regression and Final Interpretation

This module explores multinomial regression, advanced interpretation of regression outputs, and case-based applications. Learners will practice interpreting outputs like case processing summaries, model fitting, and parameter estimates to draw meaningful conclusions.