Overview

Welcome to this data-driven course from the Tabtrainer® Certified Series - your trusted platform for industrial analytics and applied regression modeling.In this course, you'll learn to build, refine, and interpret multiple linear regression models in Minitab, using a real production case from the Speedboard Company. You'll apply both manual and automated backward elimination to identify the most relevant predictors, reduce model complexity, and maintain statistical integrity.From correlation analysis and VIF-based multicollinearity checks to advanced model diagnostics and best subsets regression, this training equips you to make confident, evidence-based decisions in industrial quality, R & D, and process optimization.Led by Prof. Dr. Murat Mola, TÜV-certified expert, industrial consultant, and Professor of the Year 2023 in Germany, this course bridges academic depth with practical relevance under the trusted brand Tabtrainer®.The course Multiple Regression with Backward Elimination teaches participants how to:Analyze industrial data with multiple continuous and categorical predictors. Apply backward elimination, interpret p-values, VIFs, and residuals, and use best subsets regression for model simplification. Emphasis is placed on practical model optimization and real-world decision-making:Understand the basics of multiple regression analysis and apply it to real-world industrial data involving both continuous and categorical predictors.Conduct a full regression workflow including data import, exploration, matrix plots, and hypothesis testing to assess initial trends and relationships.Interpret correlation coefficients and determine whether linear relationships between variables are statistically significant using Pearson correlation and p-values.Evaluate the effect of individual predictors (e.g., deck width, wheel hardness, deck flex) on the response variable (maximum speed) using p-values and model coefficients.Apply and interpret the Variance Inflation Factor (VIF) to detect and assess multicollinearity between predictor variables.Perform step-by-step backward elimination, removing non-significant predictors iteratively to simplify the model while preserving statistical integrity.Use adjusted R-squared and predicted R-squared to evaluate and compare the goodness-of-fit of different regression models, ensuring model validity and predictive quality.Assess model assumptions through residual analysis, including normality, homoscedasticity, and independence, using "Four-in-One" diagnostic plots.Execute automated backward elimination and understand its benefits compared to manual iterative elimination, especially in high-dimensional models.Apply best subsets regression to identify the most influential predictors under practical constraints and interpret advanced model quality parameters such as Mallows Cp, PRESS, AICc, and BIC.

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