Multiple linear regression model violations
25 important questions on Multiple linear regression model violations
Q: What are the four main MLR model violations (Ch. 22 Nieuwenhuis)?
- Collinearity
- Heteroskedasticity
- Non-linearity & non-normality
- Dependence (autocorrelation) of error terms
Q: What is collinearity?
Q: What is heteroskedasticity?
Var(εi) ≠ σ².
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Q: What is non-linearity in regression?
Q: What is non-normality of errors?
Q: What is autocorrelation?
Q: Why is autocorrelation a problem?
- LS estimators not optimal
- t-tests and F-tests invalid
- Variances underestimated
- Results not trustworthy
Q: What does the Durbin–Watson test check?
Q: What is the range of DW?
Q: How to interpret DW values?
Q: Positive autocorrelation test H₁: positive autocorr → when do we reject H₀?
Q: Negative autocorrelation test H₁: negative autocorr → when do we reject H₀?
Q: Two-sided autocorrelation test → when reject H₀?
- D ≤ dL (positive)
- D ≥ 4 − dL (negative)
Q: What are the three possible conclusions of a DW test?
Q: What is a dummy variable?
Q: Why do we use dummy variables?
Q: What is the “base level”?
Q: Interpretation of B for a dummy variable?
Q: How do we interpret β for ‘female’ dummy (1 = female, 0 = male)?
If β < 0 → women earn less (in wage example).
Q: What does the partial F-test check?
Q: Hypotheses for partial F-test?
H₁: at least one β ≠ 0.
Q: What is the test statistic for partial F-test?
Q: What are the degrees of freedom?
Denominator: n − (k + 1)
Q: When do we reject H₀ in a partial F-test?
Q: What does a huge F value indicate?
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