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.

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.

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.

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.

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.

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.