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Linear Regression Q&A
1What is linear regression?
Answer: Model predicting continuous target as linear combination of features.
2What is OLS?
Answer: Method minimizing sum of squared residuals.
3Assumptions?
Answer: Linearity, independence, homoscedasticity, normal residuals, low multicollinearity.
4What is multicollinearity?
Answer: Highly correlated predictors making coefficients unstable.
5R-squared meaning?
Answer: Proportion of variance explained by the model.
6Adjusted R-squared?
Answer: Penalized version accounting for number of predictors.
7MAE vs RMSE?
Answer: RMSE penalizes large errors more than MAE.
8How detect heteroscedasticity?
Answer: Residual plots or statistical tests like Breusch-Pagan.
9How handle nonlinear relation?
Answer: Transform features or add polynomial/interactions.
10What is regularized regression?
Answer: Ridge/Lasso add penalties to control overfitting.
11When Lasso preferred?
Answer: For sparse feature selection by shrinking some coefficients to zero.
12One-line summary?
Answer: Linear regression is a fast baseline and interpretable predictive model.