2. Supervised Learning

Linear Regression — Quiz

Test your understanding of linear regression with 5 practice questions.

Practice Questions

Question 1

In the context of Ordinary Least Squares (OLS) regression, what is the impact of perfect multicollinearity among independent variables on the estimation of regression coefficients?

Question 2

Consider a Ridge regression model. If the regularization parameter $ \lambda $ approaches infinity, what is the expected behavior of the regression coefficients?

Question 3

In the bias-variance tradeoff, which of the following statements accurately describes the effect of increasing model complexity on bias and variance?

Question 4

Which of the following diagnostic plots is most effective for identifying non-linearity in the relationship between the independent and dependent variables in a linear regression model?

Question 5

Given a linear regression model where the true relationship is $Y = 2X + 5 + \epsilon$. If we fit a model that assumes $Y = \beta_0 + \epsilon$ (i.e., ignoring $X$), what kind of error would predominantly characterize this fitted model?