Top Linear Regression Interview Questions (Practice)

3 min read 473 words

Fundamentals / Commonly Asked Questions

How would you explain linear regression to someone without a technical background?
What assumptions does linear regression make about the data?
How do you interpret the coefficients in a linear regression model?
What role does the intercept play in a regression equation?
How do you measure the goodness of fit of a linear regression model?

Why does multicollinearity pose a problem in linear regression, and how would you detect it?
How does ordinary least squares estimation work, and why is it commonly used?
Can you explain the intuition behind the gradient descent optimization method for regression?
What are residuals, and why are they important in model diagnostics?
How would you handle categorical variables in linear regression?

What happens if the error terms in your regression model are not normally distributed?
Can linear regression still be useful if the true relationship between variables is non-linear? How?
What issues arise if you have more features than observations in linear regression?
How do outliers impact a regression model, and what strategies can mitigate their effects?
What does it mean if the R² value of a model is very high but the model performs poorly in production?

When would you prefer linear regression over logistic regression, and vice versa?
Compare ridge regression, lasso regression, and elastic net. How would you decide which one to use?
How would you choose between a simple linear regression and a polynomial regression model?
What are the trade-offs between using a parametric model like linear regression versus a non-parametric model like decision trees?
How would you explain the difference between overfitting in regression and underfitting, and how do you balance the two?

What are generalized linear models (GLMs), and how do they extend linear regression?
Can you describe how linear regression connects to maximum likelihood estimation?
What role does linear regression play as a baseline in modern machine learning research?
How does linear regression appear in causal inference frameworks (e.g., estimating treatment effects)?
Can you discuss any recent research or advancements that improve regression robustness to adversarial or high-dimensional settings?

Imagine you are building a production system where linear regression is used to predict demand. How would you ensure stability and adaptability as data distributions shift over time?
If you had infinite compute and unlimited data, would linear regression still be relevant? Why or why not?
How would you design a hybrid model that combines linear regression with deep learning to capture both interpretability and predictive power?
What do you think are the fundamental limitations of linear regression that no amount of tuning or data can fix?
Suppose your regression model consistently fails to generalize across geographies. How would you redesign your approach to account for this?