Description

Book Synopsis

A Hands-On Way to Learning Data Analysis

Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Third Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the second edition.

New to the Third Edition

  • 40% more content with more explanation and examples throughout
  • New chapter on sampling featuring simulation-based methods
  • Model assessment methods discussed
  • Explanation chapter expanded to include introductory ideas about causation
  • Model interpretation in the presence of transformation
  • Crossvalidation for model s

Linear Models with R

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    £71.24

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    RRP £74.99 – you save £3.75 (5%)

    Order before 4pm today for delivery by Mon 8 Jun 2026.

    A Hardback by Julian J. Faraway

    2 in stock


      View other formats and editions of Linear Models with R by Julian J. Faraway

      Publisher: CRC Press
      Publication Date: 2/27/2025
      ISBN13: 9781032583983, 978-1032583983
      ISBN10: 1032583983

      Description

      Book Synopsis

      A Hands-On Way to Learning Data Analysis

      Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Third Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the second edition.

      New to the Third Edition

      • 40% more content with more explanation and examples throughout
      • New chapter on sampling featuring simulation-based methods
      • Model assessment methods discussed
      • Explanation chapter expanded to include introductory ideas about causation
      • Model interpretation in the presence of transformation
      • Crossvalidation for model s

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