Interpret and Regularize Regression Models
Company: Instacart
Role: Data Scientist
Category: Statistics & Math
Difficulty: hard
Interview Round: Onsite
You are building and interpreting regression models for a product dataset. The outcome variable is a continuous user-level metric such as spend, session duration, or order value. The dataset includes user attributes, prior engagement, device type, geography, and treatment indicators.
Answer the following:
1. In a linear regression, how do you interpret a coefficient and its p-value?
2. If the outcome distribution is heavily right-skewed, how would you choose between ordinary linear regression on the raw outcome and linear regression on the log-transformed outcome? What are the downsides of using a log transform?
3. If a regression model has an R-squared close to 0, can you simply add more covariates to improve performance? What are the risks?
4. If a model already has a promising R-squared, should you use Lasso or Ridge regression? Why or why not? How would you evaluate whether regularization helps?
Quick Answer: This question evaluates competency in interpreting linear regression coefficients and p-values, selecting and interpreting outcome transformations for skewed continuous metrics, assessing model fit and risks of adding covariates, and understanding regularization methods such as Lasso and Ridge.