How would you critique this regression?

Question

You are asked to review a modeling pipeline that another data scientist put together and to critique it. Business context A website gets traffic from Google Search. The outcome variable is:

• Y = how many seconds a visitor remains on the site after arriving from Google Search, measured within that same session (session dwell time). There are 4 candidate predictors, X1–X4. Their exact definitions are not given (they might be a mix of numeric and categorical features), so part of the task is to explain what you would want clarified before signing off on the analysis. The other data scientist ran the following pipeline to build a linear regression model:

1. They saw that Y looks roughly normally distributed, and decided that ordinary least squares (OLS) was therefore suitable.
2. They fit every possible combination of the 4 predictors, including squared (quadratic) terms and all pairwise second-order interactions.
3. They picked the model with the best in-sample fit as the final model. Question Critique this pipeline. What clarifying questions would you ask before accepting the analysis, and what would you recommend instead? In your answer, address:
4. Whether the goal is prediction, inference, or causal estimation, and how that changes the right choices.
5. Which assumptions actually matter for OLS and for valid statistical inference — and why the marginal normality of Y is not one of the Gauss–Markov assumptions.
6. How dwell-time data can violate standard linear-model assumptions (skew, zeros, censoring, outliers, dependence).
7. The risks of the exhaustive subset + interaction search and the resulting model-selection / overfitting bias, including why "best in-sample fit" is the wrong selection criterion.
8. The diagnostics you would check instead (functional form, heteroskedasticity, multicollinearity/VIF, influence, clustering, leakage).
9. How you would redesign the modeling and validation process — baseline model, proper train/validation/test or cross-validation, evaluation metrics, and possible alternatives such as target transformation, GLMs, regularization, robust/clustered standard errors, or tree-based models. You may assume the sample size is not stated. Overview: An Apple Data Scientist technical-screen statistics question that asks you to critique another analyst's linear-regression workflow for modeling website dwell time. It tests OLS assumptions (and why marginal normality of Y is not one of them), model-selection and overfitting bias from exhaustive interaction search, diagnostics and validation, and the distinction between prediction, inference, and causal goals on skewed outcome data.