SIG Quantitative Researcher Intern Interview Question: Linear Regression Assumptions and Violations
Linear regression is a cornerstone of quantitative analysis and modeling, especially in financial firms like Susquehanna International Group (SIG). Understanding its foundational assumptions is crucial for aspiring quantitative researcher interns. In interviews, one of the most common—and critical—questions is about the assumptions underlying linear regression and the practical implications of violating them. This article offers an exhaustive guide to linear regression assumptions, their importance, how to detect violations, and the impact of these violations on your models. Whether you're preparing for an SIG Quantitative Researcher Intern interview or seeking to master regression for your quantitative toolset, this resource has you covered.
Linear regression is a statistical technique for modeling the relationship between a dependent variable and one or more independent variables. Simple linear regression deals with one independent variable, while multiple linear regression involves several. The method estimates parameters (coefficients) to minimize the difference between observed values and model predictions, typically using the Ordinary Least Squares (OLS) approach.
The standard linear regression equation is:
