Statistical Inference & Regression Models Flashcards
7 cards from real MS-DS Master of Data science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Statistical Inference & Regression Models flashcards as text
LASSO regression differs from ridge regression primarily because LASSO:
Answer: Can reduce some coefficients exactly to zero, performing variable selection
LASSO's L1 penalty (λΣ|β|) can set coefficients to exactly zero, effectively performing automatic variable selection unlike ridge.
A researcher applies the Box-Cox transformation to the response variable in a linear regression. The primary reason for this transformation is to:
Answer: Address non-normality and non-constant variance in residuals
The Box-Cox transformation finds an optimal power transformation of Y to better satisfy normality and homoscedasticity assumptions.
In Bayesian inference, the posterior distribution is proportional to:
Answer: The likelihood times the prior
Bayes' theorem states posterior ∝ likelihood × prior, combining observed data evidence with prior beliefs.
When using stepwise regression for variable selection, a major statistical concern is:
Answer: Inflated Type I error rates and optimistically biased model fit statistics
Stepwise selection involves multiple testing, inflating Type I error rates and producing overly optimistic R² and p-values for the selected model.
The Gauss-Markov theorem guarantees that OLS estimators are BLUE. What does BLUE stand for?
Answer: Best Linear Unbiased Estimators
Gauss-Markov proves OLS produces Best (minimum variance) Linear Unbiased Estimators when its classical assumptions hold.
A leverage point in regression analysis is an observation that:
Answer: Has an extreme value in predictor space and high influence on the fitted line
Leverage measures how far an observation's predictor values are from the mean of predictors; high leverage points can strongly influence regression estimates.
The Wald test for a logistic regression coefficient β tests H₀: β = 0 by computing:
Answer: The ratio of the coefficient to its standard error, squared
The Wald statistic is (β̂/SE(β̂))², which follows a chi-square distribution with 1 df under H₀.