Analyze Advanced Data Using Minitab Regression Models

Overview

Learners will analyze complex datasets, interpret advanced regression outputs, evaluate predictive models, and apply decision tree techniques using Minitab to solve real-world business problems. This course provides in-depth, hands-on training in advanced statistical modeling using Minitab, moving beyond basic analytics to practical, decision-focused applications. Learners explore linear, logistic, multinomial, and CART regression models, supported by scatter plots, model diagnostics, and real industry case studies. Through guided examples and structured analysis, learners develop the ability to select appropriate models, interpret statistical outputs, and translate analytical results into actionable business insights. What makes this course unique is its strong emphasis on interpretation, visualization, and real-world applicability rather than formula-driven theory alone. Industry-inspired case studies, such as market segmentation and healthcare analytics, reinforce practical understanding, while step-by-step decision tree modeling builds confidence in explainable AI techniques. By completing this course, learners gain job-ready skills in advanced data analysis, improve their statistical decision-making capabilities, and become proficient in using Minitab to support data-driven strategies across business, quality, and analytics roles.

Syllabus

Regression Foundations & Logistic Modeling in Minitab

This module introduces learners to advanced regression concepts using Minitab, focusing on statistical theory, regression outputs, scatter plot analysis, and logistic regression. Learners develop the ability to interpret regression results and apply logistic models to real-world business scenarios using visual and statistical tools.

Applied Regression & Model Comparison

This module focuses on applying regression techniques to real-world case studies, market segmentation, and multinomial regression problems. Learners compare linear and non-linear models using statistical metrics and scatter plots to select the most appropriate regression model.

Decision Trees & CART Regression in Practice

This module introduces decision tree modeling and CART regression techniques, emphasizing interpretability, model evaluation, and real-world applications. Learners analyze decision paths, interpret CART outputs, and understand the limitations of tree-based models.