In Polynomial Regression, how does model interpretation change compared to simple Linear Regression?
It becomes simpler because polynomial terms capture more variance, making the model easier to understand.
Polynomial Regression makes no difference to model interpretation.
Interpretation is not relevant in Polynomial Regression.
It becomes more complex as the relationship between the target and predictors might not be linear.
What is the primary purpose of using hierarchical linear models (HLMs)?
To analyze data with a single level of variability.
To handle missing data in a linear regression model.
To analyze data with nested or grouped structures.
To improve the accuracy of predictions in linear regression.
How does stepwise selection work in feature selection?
It ranks features based on their correlation with the target variable and selects the top-k features.
It transforms the original features into a lower-dimensional space while preserving important information.
It iteratively adds or removes features based on a statistical criterion, aiming to find the best subset.
It uses L1 or L2 regularization to shrink irrelevant feature coefficients to zero.
What is the primary reason multicollinearity poses a problem in linear regression?
It makes the model too complex.
It reduces the model's predictive accuracy on new data.
It inflates the variance of the regression coefficients, making them unreliable.
It violates the assumption of linearity between the dependent and independent variables.
In the context of GLMs, what is the purpose of the inverse link function?
To obtain predictions on the scale of the response variable
To transform the predictor variables before analysis
To assess the goodness-of-fit of the GLM
To estimate the variance of the response variable
What happens to the bias and variance of a linear regression model as the regularization parameter (lambda) increases?
Bias decreases, Variance decreases
Bias decreases, Variance increases
Bias increases, Variance decreases
Bias increases, Variance increases
The performance of the Theil-Sen estimator can be sensitive to which characteristic of the data?
The presence of multicollinearity (high correlation between independent variables)
The presence of categorical variables
The non-normality of the residuals
The presence of heteroscedasticity (unequal variances of errors)
Which evaluation metric is particularly sensitive to outliers in the dependent variable?
Adjusted R-squared
MAE
RMSE
R-squared
Which of the following is a synonym for Hierarchical Linear Models?
Time Series Models
Nonlinear Regression Models
Multilevel Models
Simple Linear Models
What is a key limitation of relying solely on Adjusted R-squared for model evaluation in linear regression?
It is difficult to interpret.
It is highly sensitive to outliers.
It doesn't provide information about the magnitude of prediction errors.
It can be misleading when comparing models with different numbers of predictors.