Analyze Predictor Impact Using Regression in Minitab

Overview

By the end of this course, learners will be able to analyze how multiple predictors influence a response variable, interpret regression outputs using Minitab, compare alternative regression models, and translate statistical findings into actionable business insights. Learners will develop the ability to evaluate predictor significance, visualize relationships, and select appropriate regression models to support data-driven decision-making. This course provides a practical, hands-on approach to regression analysis through a real-world Tech Mahindra case study. Rather than focusing on theory alone, learners work step-by-step through an end-to-end analytics project—starting from problem framing and exploratory analysis to model refinement and comparison. Using Minitab, learners gain experience interpreting regression coefficients, p-values, and visual diagnostics such as scatter plots to understand predictor–response relationships. What makes this course unique is its strong business orientation and project-based structure. Learners not only build and compare linear and quadratic regression models but also learn how to justify model choices based on both statistical performance and business relevance. This course is ideal for professionals and students seeking to strengthen applied regression skills and confidently use Minitab for real-world analytical challenges.

Syllabus

Framing the Business Problem with Regression

This module introduces learners to a real-world regression project using Minitab, focusing on clearly defining the business problem, identifying response and predictor variables, and building an initial regression model. Through the Tech Mahindra case study, learners explore how statistical analysis connects to business decision-making using exploratory data analysis, regression output interpretation, and visualization techniques.

Model Refinement and Comparative Analysis

This module guides learners through refining regression models by reassessing predictors, validating assumptions, and comparing alternative model forms. Using advanced iterations of the Tech Mahindra case study, learners evaluate linear and quadratic regression models, apply visual and statistical comparison techniques, and select models that balance analytical accuracy with business interpretability.