What does a high Cook's distance value indicate?
The observation has low leverage but high influence
The observation has high leverage but low influence
The observation has both high leverage and high influence
The observation is not an outlier
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
What does the adjusted R-squared value tell you in multiple linear regression?
The statistical significance of the overall model.
The accuracy of the model's predictions.
The presence of outliers in the data.
The proportion of variance in the outcome explained by the predictors, adjusted for the number of predictors in the model.
Logistic regression, a specific type of GLM, is best suited for modeling which type of response variable?
Continuous
Binary (two categories)
Count data
Time-to-event data
What is the primary reason multicollinearity poses a problem in linear regression?
It inflates the variance of the regression coefficients, making them unreliable.
It makes the model too complex.
It reduces the model's predictive accuracy on new data.
It violates the assumption of linearity between the dependent and independent variables.
A model has a high R-squared but a low Adjusted R-squared. What is a likely explanation?
The model is overfitting.
The model is a perfect fit.
The model has high bias.
The model is too simple.
If a linear regression model has an Adjusted R-squared value of 0.85, what does it indicate about the goodness of fit?
The model's predictions will be accurate 85% of the time.
The model explains 15% of the variation in the dependent variable.
The model is overfitting the data.
The model explains 85% of the variation in the dependent variable, accounting for the number of predictors.
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 increases
Bias increases, Variance decreases
What advantage does Polynomial Regression offer over simple Linear Regression when dealing with non-linear relationships between variables?
It simplifies the model, making it easier to interpret.
It always results in a better fit regardless of the data distribution.
It introduces polynomial terms, enabling the model to fit curved relationships in the data.
It reduces the need for feature scaling.
Which robust regression technique is particularly well-suited for handling datasets with a high proportion of outliers?
Ordinary Least Squares (OLS) regression
Theil-Sen estimator
RANSAC (Random Sample Consensus)
Huber regression