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This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Voleon Group.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Voleon Group during technical interviews.

Fit Linear Regression: Analyze Economic Impact of Coefficients

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of linear regression modeling, coefficient estimation and economic interpretation, hypothesis testing using t-statistics and p-values, nested F-tests for model comparison, and the ability to connect estimated betas to economic meaning in a financial tabular dataset.

Voleon Group

Aug 4, 2025, 10:55 AM

Data Scientist

Technical Screen

Machine Learning

Scenario

You are given a tabular financial dataset df where the column target is the dependent variable (e.g., next-period return or excess return), and all other columns are candidate predictors/features.

Task

Fit an Ordinary Least Squares (OLS) regression with target as the dependent variable and all other columns as predictors.
Report each estimated coefficient (beta) and interpret its economic meaning.
Describe how you would sequentially test the marginal contribution of each feature (i.e., whether adding/removing a feature improves the model), referencing t-tests and nested F-tests.

Notes

Use Python statsmodels OLS.
Refer to summary(), t-statistics, p-values, adjusted R², and nested F-tests. Stepwise procedures are acceptable if justified.

Solution

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