Multiple Regression in Minitab – Tabtrainer® Backward Guide
Model industrial data with Minitab using backward elimination – reduce predictors, detect multicollinearity
0
students
1 hour
content
May 2025
last update
$44.99
regular price
What you will 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
Course Gallery
6575937
udemy ID
17/04/2025
course created date
01/05/2025
course indexed date
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