Q. In a case study, if a linear regression model has a high R-squared value but a high Mean Squared Error (MSE), what does this suggest?
A.
The model is performing well overall
B.
The model may be overfitting the training data
C.
The model is underfitting the data
D.
The model is perfectly accurate
Solution
A high R-squared with high MSE suggests that while the model explains a lot of variance, it may be overfitting the training data and not generalizing well.
Correct Answer:
B
— The model may be overfitting the training data
Q. In a linear regression case study, what does multicollinearity refer to?
A.
High correlation between the dependent variable and independent variables
B.
High correlation among independent variables
C.
Low variance in the dependent variable
D.
The presence of outliers in the data
Solution
Multicollinearity refers to a situation where independent variables in a regression model are highly correlated with each other, which can affect the model's estimates.
Correct Answer:
B
— High correlation among independent variables
Q. In a linear regression model, what does the slope coefficient represent?
A.
The intercept of the regression line
B.
The change in the dependent variable for a one-unit change in the independent variable
C.
The total variance in the dependent variable
D.
The correlation between the independent and dependent variables
Solution
The slope coefficient indicates how much the dependent variable is expected to increase (or decrease) when the independent variable increases by one unit.
Correct Answer:
B
— The change in the dependent variable for a one-unit change in the independent variable
Q. Which evaluation metric is commonly used to assess the performance of a linear regression model?
A.
Accuracy
B.
F1 Score
C.
Mean Absolute Error (MAE)
D.
Confusion Matrix
Solution
Mean Absolute Error (MAE) is a common metric for evaluating the performance of regression models, measuring the average magnitude of errors in predictions.
