Apply Hypothesis Testing with Minitab for Data Analysis

Overview

Learners will analyze real-world datasets using Minitab, apply correlation and regression techniques, formulate and test statistical hypotheses, and interpret p-values and confidence intervals to make data-driven decisions. By the end of this course, learners will confidently evaluate statistical evidence and translate analytical results into meaningful conclusions. This course offers a hands-on, practical approach to hypothesis testing using Minitab, designed for learners who want to move beyond theory and apply statistics in real scenarios. Through a guided project, learners explore data relationships, perform exploratory analysis, and progressively build skills in hypothesis testing—from defining null and alternative hypotheses to executing tests and interpreting results. Each concept is reinforced through step-by-step demonstrations, practical examples, and applied decision-making exercises. What makes this course unique is its project-driven structure, software-focused learning, and clear linkage between statistical concepts and business interpretation. Rather than focusing on formulas alone, learners gain practical experience using industry-relevant tools and workflows. This course is ideal for students, quality professionals, analysts, and engineers seeking to apply hypothesis testing confidently using Minitab in real-world data analysis tasks.

Syllabus

Foundations of Data Analysis & Hypothesis Testing

This module introduces learners to the practical application of statistical analysis using Minitab by establishing project context, exploring relationships between variables, and building a strong foundation in hypothesis testing. Learners will analyze real-world data, interpret correlation and regression results, and understand how exploratory analysis leads to formal statistical decision-making.

Performing Hypothesis Testing in Minitab

This module focuses on executing and interpreting hypothesis tests using Minitab. Learners will apply statistical testing procedures, understand the role of summary statistics and confidence intervals, formulate and evaluate null hypotheses, and use p-values to make evidence-based decisions in practical data analysis scenarios.