This module teaches learners how to differentiate between paired- and independent-samples t-tests, how to apply these tests to real-world data scenarios, and how to interpret and report statistical results effectively. It covers hypothesis testing, data analysis using software tools like Excel and SPSS, and practical case studies to reinforce statistical reasoning.

This module explores the fundamentals of Analysis of Variance (ANOVA), including how to compare group means, interpret F-statistics and p-values, and assess the assumptions required for valid statistical analysis. Learners will also gain hands-on experience with one-way ANOVA procedures, effect size calculations, and post-hoc testing to draw meaningful conclusions from data.

This module teaches learners how to apply chi-squared tests of fit and independence to analyze categorical data. It covers the step-by-step process of hypothesis testing, including setting significance levels, interpreting results, and using statistical tools like Excel and SPSS. Learners will develop the skills to evaluate real-world data and make informed decisions based on statistical evidence.

This module explores statistical techniques for analyzing relationships between variables, including correlation and regression. Learners will gain skills in interpreting statistical results, assessing model assumptions, and applying these methods to real-world scenarios. The content emphasizes both theoretical foundations and practical implementation.

This module equips learners with the skills to select and apply the most appropriate statistical tests for business problems, emphasizing the importance of data context, measurement scales, and test assumptions. It also guides learners through the decision-making process in statistical analysis and highlights common pitfalls in data interpretation.