Which robust regression technique is particularly well-suited for handling datasets with a high proportion of outliers?
RANSAC (Random Sample Consensus)
Ordinary Least Squares (OLS) regression
Theil-Sen estimator
Huber regression
A model has a high R-squared but a low Adjusted R-squared. What is a likely explanation?
The model has high bias.
The model is too simple.
The model is overfitting.
The model is a perfect fit.
Which of the following is a common indicator of multicollinearity when examining a correlation matrix?
Negative correlation coefficients between the dependent and independent variables
High correlation coefficients between the dependent variable and independent variables
High correlation coefficients between some independent variables
Low correlation coefficients between all independent variables
What happens to the bias and variance of a linear regression model as the regularization parameter (lambda) increases?
Bias decreases, Variance decreases
Bias increases, Variance increases
Bias decreases, Variance increases
Bias increases, Variance decreases
What does multicollinearity refer to in the context of multiple linear regression?
A high correlation between two or more predictor variables.
Non-linearity in the relationship between predictors and outcome.
A high correlation between the outcome variable and a predictor variable.
The presence of outliers in the data.
What is the primary motivation for using robust regression over ordinary least squares (OLS) regression?
To reduce the computational complexity of the regression analysis
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
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)
In Polynomial Regression, how does model interpretation change compared to simple Linear Regression?
Interpretation is not relevant in Polynomial Regression.
It becomes more complex as the relationship between the target and predictors might not be linear.
It becomes simpler because polynomial terms capture more variance, making the model easier to understand.
Polynomial Regression makes no difference to model interpretation.
What hyperparameter controls the strength of regularization in Ridge, Lasso, and Elastic Net Regression?
Number of Iterations
Regularization Parameter
Tolerance
Learning Rate
Which of the following is a synonym for Hierarchical Linear Models?
Multilevel Models
Time Series Models
Nonlinear Regression Models
Simple Linear Models