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Compute robust linear interpolation with edge cases

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of linear interpolation, representation as convex combinations, handling of degenerate input cases, bounding interpolation error under bounded measurement noise, and piecewise-linear interpolation for identifying interpolation versus extrapolation, relevant to data scientist roles in the Statistics & Math domain. It is commonly asked to test numerical reasoning, robustness to edge cases and noise, and the ability to connect interpolation theory with practical numerical methods, thus probing both conceptual understanding and practical application.

  • Medium
  • Two Sigma
  • Statistics & Math
  • Data Scientist

Compute robust linear interpolation with edge cases

Company: Two Sigma

Role: Data Scientist

Category: Statistics & Math

Difficulty: Medium

Interview Round: Take-home Project

You are given two distinct points (x0, y0) and (x1, y1) with x0 != x1. a) Derive the linear interpolation function f(x) on [min(x0, x1), max(x0, x1)] and compute f(19) when (x0, y0) = (10, 100) and (x1, y1) = (25, 175). Provide both the exact rational form and a decimal rounded to 3 decimals. b) Prove that f(x) can be written as a convex combination of y0 and y1 via t = (x − x0)/(x1 − x0). c) Specify a consistent rule for the degenerate case x0 = x1 with y0 != y1 (e.g., error, average, or limiting value) and justify your choice. d) If the observed y-values have independent bounded errors |ε| ≤ 0.5, give a bound on the interpolation error at any x. e) Using piecewise-linear interpolation on the sorted points (0,0), (3,9), (8,4), (10,10), compute f(6) and f(9), and state whether each evaluation is interpolation or extrapolation.

Quick Answer: This question evaluates understanding of linear interpolation, representation as convex combinations, handling of degenerate input cases, bounding interpolation error under bounded measurement noise, and piecewise-linear interpolation for identifying interpolation versus extrapolation, relevant to data scientist roles in the Statistics & Math domain. It is commonly asked to test numerical reasoning, robustness to edge cases and noise, and the ability to connect interpolation theory with practical numerical methods, thus probing both conceptual understanding and practical application.

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Two Sigma
Oct 13, 2025, 9:49 PM
Data Scientist
Take-home Project
Statistics & Math
4
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You are given two distinct points (x0, y0) and (x1, y1) with x0 != x1. a) Derive the linear interpolation function f(x) on [min(x0, x1), max(x0, x1)] and compute f(19) when (x0, y0) = (10, 100) and (x1, y1) = (25, 175). Provide both the exact rational form and a decimal rounded to 3 decimals. b) Prove that f(x) can be written as a convex combination of y0 and y1 via t = (x − x0)/(x1 − x0). c) Specify a consistent rule for the degenerate case x0 = x1 with y0 != y1 (e.g., error, average, or limiting value) and justify your choice. d) If the observed y-values have independent bounded errors |ε| ≤ 0.5, give a bound on the interpolation error at any x. e) Using piecewise-linear interpolation on the sorted points (0,0), (3,9), (8,4), (10,10), compute f(6) and f(9), and state whether each evaluation is interpolation or extrapolation.

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