What is the primary purpose of using hierarchical linear models (HLMs)?
To handle missing data in a linear regression model.
To analyze data with nested or grouped structures.
To analyze data with a single level of variability.
To improve the accuracy of predictions in linear regression.
Which metric is more interpretable in terms of the original units of the dependent variable?
Adjusted R-squared
None of the above
Root Mean Squared Error (RMSE)
Both RMSE and MAE are equally interpretable.
How does stepwise selection work in feature selection?
It transforms the original features into a lower-dimensional space while preserving important information.
It uses L1 or L2 regularization to shrink irrelevant feature coefficients to zero.
It iteratively adds or removes features based on a statistical criterion, aiming to find the best subset.
It ranks features based on their correlation with the target variable and selects the top-k features.
What does a Variance Inflation Factor (VIF) value greater than 10 generally suggest?
Heteroscedasticity
Perfect multicollinearity
No multicollinearity
Severe multicollinearity
What is the primary goal of regularization techniques in linear regression?
To prevent overfitting by adding a penalty to the complexity of the model.
To improve model interpretability by selecting only the most relevant features.
To handle missing data points in the dataset more effectively.
To speed up the training process of the linear regression model.
Which of these is NOT a recommended approach for dealing with outliers in linear regression?
Investigating the cause of the outlier and correcting errors if possible
Using robust regression methods less sensitive to outliers
Transforming the data to reduce the outlier's influence
Automatically removing all outliers without investigation
If a predictor has a p-value of 0.02 in a multiple linear regression model, what can you conclude?
The predictor is not statistically significant.
The predictor explains 2% of the variance in the outcome.
The predictor has a practically significant effect on the outcome.
The predictor is statistically significant at the 0.05 level.
Elastic Net Regression combines the penalties of which two regularization techniques?
Lasso Regression and Ridge Regression
Ridge Regression and Polynomial Regression
Linear Regression and Ridge Regression
Lasso Regression and Polynomial Regression
You are comparing two linear regression models for predicting house prices. Model A has a lower RMSE than Model B. What does this imply about their predictive performance?
Model B is definitely overfitting the data.
Model A, on average, has smaller prediction errors than Model B.
Model A is guaranteed to make better predictions on all new data points.
Model A has a higher R-squared value than Model B.
Which evaluation metric is particularly sensitive to outliers in the dependent variable?
RMSE
MAE
R-squared