Linear regression is the most important tool in quantitative research—and, as a result, it is also the most frequently tested topic in interviews. Despite the growing popularity of complex machine learning models, linear regression remains a cornerstone of quantitative analysis due to its interpretability, transparency, and ease of implementation. Whether you're building a predictive model, constructing a risk factor, or analyzing the sensitivity of returns to certain exposures, a deep understanding of regression is essential.
It’s absolutely necessary to know the ins and outs of linear regression. Of course, you need to know the basics (e.g., “state the assumptions of OLS”), but interviews often go deeper. You’ll be asked to interpret outputs, assess robustness, and identify edge cases where the model might fail. Often, even when the question doesn’t explicitly mention regression, solving it well requires regression intuition under the hood.
This section is structured into several parts:
Core assumptions and mechanics of OLS
Applied reasoning with regression setups
Practical edge cases and conceptual pitfalls
Extensions such as regularization and weighted least squares
The goal is not to memorize formulas but to internalize how regression behaves—so you can reason flexibly and rigorously in unfamiliar or messy situations, just as you would in actual research work.
The best resource for understanding the fundamentals of linear regression is Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge. This widely used undergraduate textbook with clear language, intuitive explanations, and extensive examples and exercises.
Key Chapters from the Fifth Edition
The textbook is organized into three main parts:
Chapter 1: The Nature of Econometrics and Economic Data
Part 1: Regression Analysis with Cross-Sectional Data
Chapter 2: The Simple Regression Model
Chapter 3: Multiple Regression Analysis: Estimation
Chapter 4: Multiple Regression Analysis: Inference
Chapter 5: Multiple Regression Analysis: OLS Asymptotics
Chapter 6: Multiple Regression Analysis: Further Issues
Chapter 7: Multiple Regression Analysis with Qualitative Information (Binary/Dummy Variables)
Chapter 8: Heteroskedasticity
Chapter 9: More on Specification and Data Issues
Part 2: Regression Analysis with Time Series Data
Chapter 10: Basic Regression Analysis with Time Series Data
Chapter 11: Further Issues in Using OLS with Time Series Data
Chapter 12: Serial Correlation and Heteroskedasticity in Time Series Regressions
Part 3: Advanced Topics
Chapter 13: Pooling Cross Sections Across Time: Simple Panel Data Methods
Chapter 14: Advanced Panel Data Methods
Chapter 15: Instrumental Variables Estimation and Two-Stage Least Squares
Chapter 16: Simultaneous Equations Models
Chapter 17: Limited Dependent Variable Models and Sample Selection Corrections
Chapter 18: Advanced Time Series Topics
Chapter 19: Carrying Out an Empirical Project
For interview preparation, Part 1 (Regression Analysis with Cross-Sectional Data) and Part 2 (Regression Analysis with Time Series Data) should be your top priority. These topics frequently appear in interviews. While Part 3 is less commonly tested in interviews, it covers concepts that are valuable on the job and worth exploring once you're comfortable with the basics.
Of course, this textbook alone is not sufficient to prepare for all linear regression-related interview questions. Many modern techniques—such as regularized regression (e.g., Lasso, Ridge) and robust regression—are not covered in this textbook. However, the core concepts this textbook teaches are absolutely essential. A strong foundation in OLS, inference, and diagnostics is the first step before tackling more advanced or specialized methods.
We’ll explore these additional topics in later sections, but for now, it’s critical to ensure your understanding of the fundamentals is solid.
For additional resources, see here.