Which of the following robust regression methods iteratively identifies and removes outliers before fitting a standard linear regression model?
Theil-Sen estimator
Huber regression
RANSAC (Random Sample Consensus)
None of the above
What hyperparameter controls the strength of regularization in Ridge, Lasso, and Elastic Net Regression?
Learning Rate
Number of Iterations
Regularization Parameter
Tolerance
What is the primary motivation for using robust regression over ordinary least squares (OLS) regression?
To improve the interpretability of the regression coefficients
To handle datasets with non-linear relationships between variables more effectively
To mitigate the impact of outliers on the fitted regression line
To reduce the computational complexity of the regression analysis
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 A has a higher R-squared value than Model B.
Model A is guaranteed to make better predictions on all new data points.
Model B is definitely overfitting the data.
Model A, on average, has smaller prediction errors than Model B.
Which metric is in the same units as the dependent variable, making it easier to interpret directly?
Adjusted R-squared
R-squared
RMSE
MAE
What happens to the bias and variance of a linear regression model as the regularization parameter (lambda) increases?
Bias increases, Variance decreases
Bias decreases, Variance decreases
Bias increases, Variance increases
Bias decreases, Variance increases
What is the primary role of a link function in a Generalized Linear Model?
It determines the optimal number of predictor variables to include in the model.
It establishes a connection between the linear predictor and the mean of the response variable.
It transforms the predictor variables to follow a normal distribution.
It calculates the residuals between the observed and predicted values.
How do Generalized Linear Models (GLMs) extend the capabilities of linear regression?
By limiting the analysis to datasets with a small number of observations.
By enabling the response variable to follow different distributions beyond just normal distribution.
By allowing only categorical predictor variables.
By assuming a strictly linear relationship between the response and predictor variables.
What does a Variance Inflation Factor (VIF) value greater than 10 generally suggest?
Severe multicollinearity
Heteroscedasticity
No multicollinearity
Perfect multicollinearity
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
Transforming the data to reduce the outlier's influence
Using robust regression methods less sensitive to outliers
Automatically removing all outliers without investigation