Ordinary Least Squares Quant Interview Guide
Ordinary least squares quant interview guide for fitted values, residuals, squared error, assumptions, estimator intuition, and examples.
Candidates seeing regression mechanics and interpretation prompts.
OLS minimizes squared residuals
Ordinary least squares chooses coefficients that minimize the sum of squared differences between observed outcomes and fitted values. This makes residuals central to both fitting and diagnosis.
Fitted values are model predictions
The fitted value is the model prediction for an observation given its predictors. The residual is the observed value minus that fitted value, so residual structure reveals what the model is missing.
Concrete example
If a signal model repeatedly underpredicts large moves, the residuals may show nonlinearity, omitted variables, or changing volatility. That observation is more useful than just quoting an equation.
Assumptions affect inference
OLS can still produce fitted coefficients when assumptions are weak, but standard errors and interpretation may suffer. Discuss independence, error behavior, omitted variables, and sample selection when relevant.
Common mistakes
Candidates often treat OLS as a magic fitting method. In interviews, explain the objective, then discuss whether the fitted relationship is stable and useful for the decision.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.