Extending the multiple lineair regression model
25 important questions on Extending the multiple lineair regression model
Q: What is the purpose of the partial F-test?
Q: What is the complete vs. reduced model?
- Complete model: includes all predictors X₁ … Xₖ
- Reduced model: excludes the variables we want to test (Xg+1 … Xk)
Q: What is the null hypothesis in a partial F-test?
(the variables add no useful information)
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Q: What is the test statistic for the partial F-test?
Q: What df are used in the partial F-test?
Q: When do we reject H₀ in a partial F-test?
Q: Is the partial F-test one-sided or two-sided?
Q: Why must both models (reduced + complete) be estimated?
Q: What does “jointly significant” mean?
Q: What is strict collinearity?
(e.g., X₃ = X₂ + X₄).
Q: What is collinearity (non-strict)?
Q: Why is collinearity a problem for regression?
- SE(Bⱼ) becomes large
- t-values shrink toward 0
- Individual significance becomes harder to find
- Interpretation becomes difficult
Q: What happens to the model usefulness under collinearity?
Q: Practical solution for collinearity?
Q: How does collinearity affect SE(Bⱼ)?
Q: What is a higher-order regression model?
Q: Why do higher-order terms not violate MLR assumptions?
Q: How do you test whether X² or X³ should stay in the model?
If not significant → drop the term.
Q: What is an interaction term?
Q: Basic interaction model with two variables?
Q: Basic interaction model with two variables?
Q: Meaning of β₃ (interaction coefficient)?
Q: How to test whether interaction terms are jointly useful?
- full model (with interactions)
- reduced model (without interactions)
Q: Why can interaction terms cause collinearity?
Q: What are the four key residual checks?
- Linearity
- Homoskedasticity
- Normality of errors
- Independence of errors
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