Explain linear regression to non‑technical stakeholders
Overview
This question evaluates understanding of linear regression fundamentals and related competencies, including defining target, features, coefficients, intercept, residuals, prediction versus confidence intervals, diagnostics for assumptions (linearity, independence, homoscedasticity, normal residuals) and their visualization, detection and impact of multicollinearity and outliers/influential points, data leakage, validation strategies (including time-based cross-validation), model-selection and regularization options (ridge, lasso), and the ability to translate a coefficient into a concise business narrative for non-technical stakeholders. Commonly asked in the Machine Learning domain because it assesses both conceptual understanding and practical application, verifying statistical reasoning, diagnostic rigor, model-selection trade-offs, and communication skills required to convey uncertainty and business impact to executives.
Explain linear regression to a non-technical executive using a concrete business example (e.g., predicting weekly sales from price, ad spend, and store traffic). In your explanation: 1) define the target, features, coefficients, intercept, residuals, and prediction interval; 2) state and test the assumptions (linearity, independence, homoscedasticity, normal residuals), including how you would diagnose violations with residual plots and fix them (feature transforms, interactions); 3) address multicollinearity (how you’d detect it and its impact on interpretability), outliers/influential points (e.g., Cook’s distance), and leakage; 4) communicate uncertainty to the exec (prediction intervals vs. confidence intervals) and how you’d validate the model (time-based cross-validation) and choose between OLS vs. regularized variants (ridge/lasso) when feature counts are high; 5) provide a 2–3 sentence, plain-language narrative of what a one-unit change in a feature means for the business and what caveats you’d include.