Regression & Logistic Models in Excel & Minitab

Overview

By the end of this course, learners will be able to apply advanced regression techniques, interpret outputs, diagnose model issues, and implement logistic regression for real-world business applications. They will also master statistical tools in Excel and Minitab, enabling them to perform t-tests, ANOVA, correlation, and predictive modeling with confidence. This course equips learners with both theoretical understanding and hands-on practice in predictive analytics. Through practical datasets, scatterplots, and business-focused case studies, learners will gain the ability to transform raw data into actionable insights. They will develop critical skills in identifying predictor significance, handling multicollinearity, and generating accurate regression equations. What makes this course unique is its balance of applied examples, rigorous diagnostics, and practical tool demonstrations. From consumer purchase analysis to business decision-making scenarios, learners will see how regression techniques directly support strategic outcomes. By completing this course, learners will be prepared to evaluate data-driven models, interpret complex statistical outputs, and apply regression analysis to solve real-world challenges.

Syllabus

Advanced Regression Techniques

This module introduces learners to advanced regression methods, focusing on predicted values, scatterplots, regression outputs, and diagnostics. Learners will gain practical skills in interpreting coefficients, testing significance, and identifying issues such as multicollinearity to ensure robust regression modeling.

Interpreting Regression Results

This module emphasizes applied examples of regression, guiding learners through practical dataset analysis and interpretation. Learners will practice predicting values, visualizing results, and comparing models to develop strong data-driven decision-making skills.

Logistic Regression & Applications

This module explores logistic regression foundations and real-world applications. Learners will understand categorical outcomes, interpret odds ratios, generate logistic models, and apply business-relevant case studies for actionable insights.

Business Applications & Data Analysis Tools

This module focuses on applying regression and statistical tools to real-world business and consumer data. Learners will use Excel and Minitab for analysis, implement tests like t-test, ANOVA, and correlation, and generate actionable insights for business applications.