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Estimate and Derive Regression Coefficient for X on y

Last updated: Mar 29, 2026

Quick Overview

Estimate and Derive Regression Coefficient for X on y evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  • medium
  • Upstart
  • Statistics & Math
  • Data Scientist

Estimate and Derive Regression Coefficient for X on y

Company: Upstart

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Onsite

##### Scenario Onsite round focused on statistics and probability for a research-scientist role ##### Question The data-generating process is y = X + ε, where X ~ N(0, 1) and ε ~ N(0, 1) are independent. If you instead regress X on y using ordinary least squares, what is the regression coefficient and how do you derive it? 2. In a village every family has either 1, 2, or 3 children. A random sample of 100 children yields: 50 came from 1-child families, 30 from 2-child families, and 20 from 3-child families. (a) Estimate the proportion of 1-child families in the village. (b) Construct a 95% confidence interval for that proportion. (c) How could you obtain an exact interval by modeling the family-size proportions with a Dirichlet prior and deriving the posterior credible interval? ##### Hints Use β = Cov(X, y) / Var (y) for Q1. For Q2, convert child counts to family counts via Bayes or likelihood equations, then apply multinomial/Dirichlet formulas.

Quick Answer: Estimate and Derive Regression Coefficient for X on y evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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|Home/Statistics & Math/Upstart

Estimate and Derive Regression Coefficient for X on y

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Upstart
Aug 4, 2025, 10:55 AM
mediumData ScientistOnsiteStatistics & Math
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Estimate and Derive Regression Coefficient for X on y

Statistics & Probability Onsite — Two-Part Question

Context

  • You have a simple linear data-generating process: y = X + ε, where X and ε are independent standard normals.
  • Separately, you are surveying a village where each family has 1, 2, or 3 children. Your sample is drawn uniformly at random from children (not families).

Questions

  1. Regress X on y (ordinary least squares with intercept). What is the regression coefficient β and how do you derive it?
  2. In a village, every family has 1, 2, or 3 children. You randomly sample 100 children and observe:
    • 50 from 1-child families
    • 30 from 2-child families
    • 20 from 3-child families
    Let π = (π1, π2, π3) be the proportions of families with 1, 2, and 3 children in the village. Because you sampled children, the observed proportions of children from each family size are not equal to π. Answer: (a) Estimate the proportion π1 of 1-child families. (b) Construct a 95% confidence interval for π1. (c) Describe how to obtain an “exact” Bayesian interval by using a Dirichlet prior and deriving a posterior credible interval for π1.

Hints

  • For Q1, use β = Cov(X, y) / Var(y).
  • For Q2, if q_k is the child-based proportion observed for family size k, then q_k ∝ k π_k. Convert q to π via π_k ∝ q_k / k, then normalize. For (c), use a Dirichlet prior on child-based probabilities and transform to π.

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

  • Clarify the random variables, distributional assumptions, independence assumptions, and desired output.
  • Show enough derivation for the interviewer to follow the reasoning.
  • Explain how you would validate the result with simulation or sensitivity checks.

What a Strong Answer Covers

  • A correct setup with definitions, formulas, and boundary conditions.
  • A step-by-step derivation or estimation plan.
  • Interpretation of the result, including uncertainty and practical limitations.
  • Checks for assumptions, edge cases, and numerical stability.

Follow-up Questions

  • How would the result change if the assumptions were relaxed?
  • Can you verify the answer with a simulation?
  • What is the most likely source of estimation error?
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