MSC ASM_QUIZ_3

1) Which of the following is NOT an assumption of multiple linear regression?

• Linearity

• Normality of residuals

• Homoscedasticity

• No outliers in the data

2) Multicollinearity violates which assumption of multiple linear regression?

• Linearity

• Independence of errors

• No perfect correlation among independent variables

• Homoscedasticity

3) If multicollinearity is present in a multiple linear regression model, what problem occurs?

• The independent variables become dependent on the dependent variable

• The model becomes nonlinear

• The regression coefficients become unstable and difficult to interpret

• The model will have heteroscedasticity

4) How can you check for the normality assumption in multiple linear regression?

• Scatter plots

• Q-Q plots and Shapiro-Wilk test

• Durbin-Watson test

• Variance Inflation Factor (VIF)

5) The assumption of independence in multiple linear regression implies that:

• The independent variables should not be correlated

• The residuals (errors) should not be correlated with each other

• The independent variables should have equal variance
  

• The sample size should be large

6) Which test is commonly used to detect autocorrelation in residuals?

• Shapiro-Wilk test

• Levene’s test

• Variance Inflation Factor (VIF)

• Durbin-Watson test
  

7) What does a Variance Inflation Factor (VIF) greater than 10 indicate?

• There is no multicollinearity

• The residuals are homoscedastic

• There is strong multicollinearity

• The regression model is well-fitted

8) Homoscedasticity refers to:

• The residuals having constant variance across all levels of the independent variables

• The absence of correlation among independent variables

• The independence of errors over time
  

• The normality of residuals

9) Which metric is used to evaluate the goodness-of-fit in multiple linear regression?

• Adjusted R²

• Mean Absolute Error (MAE)

• Root Mean Square Error (RMSE)

• All the above

10) Multicollinearity occurs when:

• The dependent variable is categorical

• The number of predictors is too small

• The independent variables are highly correlated

• The model is underfitting