Overview

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Master predictive analytics with SPSS through a structured progression from data management and descriptive statistics to correlation, linear and multiple regression, logistic regression, and multinomial regression. You will learn to import and organize datasets, calculate mean and standard deviation, create scatter plots, examine relationships between variables, construct predictive models, refine predictors, calculate predicted values, and interpret coefficients, significance levels, odds ratios, model-fitting results, and parameter estimates. Designed for students, researchers, and professionals who want to use SPSS for research, business, academic, health, psychology, or financial analysis, this course connects statistical concepts with practical decision-making. Guided SPSS demonstrations and hands-on case studies involving heart pulse, student test scores, copper expansion, energy consumption, debt assessment, credit card data, smoking preferences, and health outcomes help you apply each technique to varied datasets. By the end of the course, you will be able to select and apply appropriate predictive modeling techniques, evaluate predictors, interpret SPSS regression outputs, validate results, and turn raw data into meaningful insights. Its step-by-step design, diverse case-based practice, and balance of statistical interpretation with software application make it a practical path from SPSS fundamentals to advanced regression analysis.

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.