Model industrial data with Minitab using backward elimination – reduce predictors, detect multicollinearity

What you'll learn
- 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 p-values
- Evaluate the effect of individual predictors on the response variable using p-values and model coe
- 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 predictiv
- 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 Ma

Requirements
- No Specific Prior Knowledge Needed: all topics are explained in a practical step-by-step manner.

Description
Welcome to this data-driven course from theTabtrainer® Certified Series– your trusted platform for industrial analytics and applied regression modeling.

In this course, you'll learn to build, refine, and interpretmultiple linear regression modelsinMinitab, using a real production case from theSpeedboard Company. You'll apply bothmanual and automated backward eliminationto identify the most relevant predictors, reduce model complexity, and maintain statistical integrity.

From correlation analysis andVIF-based multicollinearity checksto advanced model diagnostics andbest subsets regression, this training equips you to make confident, evidence-based decisions in industrial quality, R&D, and process optimization.

Led byProf. Dr. Murat Mola, TÜV-certified expert, industrial consultant, andProfessor of the Year 2023 in Germany, this course bridges academic depth with practical relevance under the trusted brandTabtrainer®.

The course Multiple Regression with Backward Elimination teaches participants how to:

Analyze industrial datawith 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 analysisand apply it to real-world industrial data involving both continuous and categorical predictors.

Conduct a full regression workflowincluding data import, exploration, matrix plots, and hypothesis testing to assess initial trends and relationships.

Interpret correlation coefficientsand 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-squaredto 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 eliminationand understand its benefits compared to manual iterative elimination, especially in high-dimensional models.

Apply best subsets regressionto identify the most influential predictors under practical constraints and interpret advanced model quality parameters such as Mallows Cp, PRESS, AICc, and BIC.

Who this course is for:
- Six Sigma Practitioners: Professionals looking to enhance their expertise in statistical tools for process optimization and decision-making.
- Quality Assurance Professionals: Those responsible for monitoring production processes and ensuring product quality will gain practical tools for defect analysis.
- Production Managers: Managers overseeing manufacturing operations will benefit from learning how to identify and address quality issues effectively.
- Engineers and Analysts: Individuals in manufacturing or technical roles seeking to apply statistical methods to real-world challenges in production.
- Business Decision-Makers: Executives and leaders aiming to balance quality, cost, and efficiency in production through data-driven insights and strategies.
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