{
  "schema": "ml-prep/item@1",
  "item": {
    "id": "mit-18-05-s2022-pset6-q2",
    "area": "mathematics",
    "topic": "statistics",
    "origin": "external",
    "title": "Simple linear regression from Gaussian maximum likelihood",
    "skills": [
      "simple-linear-regression",
      "maximum-likelihood",
      "likelihood"
    ],
    "priority": "core",
    "difficulty": "medium",
    "estimated_minutes": 25,
    "prerequisites": [
      "gaussian-distribution",
      "partial-derivatives"
    ],
    "source_id": "mit-18-05-s2022",
    "locator": {
      "document": "Problem Set 6",
      "label": "Problem 2(a–b)",
      "page_label": "problem PDF pp. 1–2",
      "solution_pages": "1–3"
    },
    "links": {
      "problem": "https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2022/resources/mit18_05_s22_pset06_pdf/",
      "solution": "https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2022/resources/mit18_05_s22_pset06_sol_pdf/"
    },
    "selection_note": "Derives a fitted line from an explicit Gaussian error model, linking regression, likelihood, and least squares.",
    "answer_status": "official-solution"
  }
}