MS-DS Master of Data science FREE Master of Data Science: Statistical Inference & Regression Models Questions and Answers 3

Question 1

In ridge regression, what effect does increasing the regularization parameter lambda have on the coefficient estimates?

Accepted Answer

Coefficients are shrunk toward zero but remain nonzero

Answer

Increasing lambda in ridge regression penalizes large coefficients, shrinking them toward zero without setting any exactly to zero.

Question 2

What is the interpretation of a 95% prediction interval for a new observation in linear regression?

Accepted Answer

A new individual observation will fall within this interval with approximately 95% probability

Answer

A 95% prediction interval accounts for both estimation uncertainty and individual variation, capturing where a single new observation is expected to fall.

Question 3

Which method is most appropriate for handling non-constant variance (heteroscedasticity) in a regression model?

Accepted Answer

Using weighted least squares estimation

Answer

Weighted least squares assigns different weights to observations based on the inverse of their variance, directly addressing heteroscedasticity.

Question 4

In the context of hypothesis testing for regression coefficients, what does a Type II error represent?

Accepted Answer

Failing to reject the null hypothesis when it is false

Answer

A Type II error occurs when we fail to reject a false null hypothesis, meaning we miss a real effect that exists in the population.

Question 5

What is the key advantage of using adjusted R-squared over R-squared when comparing regression models?

Accepted Answer

It penalizes the addition of predictors that do not improve the model

Answer

Adjusted R-squared penalizes for adding predictors that do not meaningfully improve model fit, preventing artificial inflation of the goodness-of-fit measure.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science FREE Master of Data Science: Statistical Inference & Regression Models Questions and Answers 3