Q. What is multicollinearity in the context of linear regression?
A.
When the dependent variable is not normally distributed
B.
When independent variables are highly correlated with each other
C.
When the model has too many predictors
D.
When the residuals are not independent
Solution
Multicollinearity occurs when two or more independent variables in a regression model are highly correlated, which can affect the stability of coefficient estimates.
Correct Answer:
B
— When independent variables are highly correlated with each other
Q. What is the assumption of homoscedasticity in linear regression?
A.
The residuals have constant variance across all levels of the independent variable
B.
The residuals are normally distributed
C.
The relationship between the independent and dependent variable is linear
D.
The independent variables are uncorrelated
Solution
Homoscedasticity refers to the assumption that the residuals (errors) of a regression model have constant variance across all levels of the independent variable.
Correct Answer:
A
— The residuals have constant variance across all levels of the independent variable
Q. Which of the following metrics is commonly used to evaluate the performance of a linear regression model?
A.
Accuracy
B.
F1 Score
C.
Mean Squared Error (MSE)
D.
Confusion Matrix
Solution
Mean Squared Error (MSE) is a common metric used to evaluate the performance of regression models by measuring the average squared difference between predicted and actual values.
