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Linear models with R / Julian J. Faraway.

By: Faraway, Julian James [author].
Material type: materialTypeLabelBookSeries: Texts in statistical science: Publisher: Boca Rotan, FL ; London, UK : CRC Press, 2025Copyright date: ©2025Edition: Third edition.Description: x, 378 pages : illustrations ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781032583983 (hardback); 1032583983 (hardback).Subject(s): R (Computer program language) | Linear models (Statistics) | Linear models (Statistics) -- Data processing | Analysis of varianceDDC classification: 519.538
Contents:
Introduction -- Estimation -- Inference -- Sampling -- Prediction -- Explanation and causation -- Diagnostics -- Predictor issues -- Modelling with the error -- Transformation -- Model selection -- Regularization -- Insurance redlining - a complete example -- Missing data -- Categorical predictors -- One factor models -- Models with several factors -- Experiments with blocks.
Holdings
Item type Current library Call number Status Notes Barcode
General lending MTU Bishopstown Library Lending 519.538 (Browse shelf(Opens below)) Available MTU Cork Module STAT8011 - Core reading. 00198244
Total holds: 0

Enhanced descriptions from Syndetics:

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 selection Chapter on regularization now includes the elastic net More on multiple comparisons and the use of marginal means Discussion of design and power

Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.

Includes bibliographic references (pages 369-374) and index.

Introduction -- Estimation -- Inference -- Sampling -- Prediction -- Explanation and causation -- Diagnostics -- Predictor issues -- Modelling with the error -- Transformation -- Model selection -- Regularization -- Insurance redlining - a complete example -- Missing data -- Categorical predictors -- One factor models -- Models with several factors -- Experiments with blocks.

MTU Cork Module STAT8011 - Core reading.

Author notes provided by Syndetics

Julian J. Faraway is a professor of statistics in the Department of Mathematical Sciences at the University of Bath. He is an applied statistician with particular application to human motion, air pollution, anxiety and depression, astronomy, cleft lip and palate, flooding, fungicides, fuel filters, marketing, obesity and wastewater-based epidemiology. He earned a PhD in statistics from the University of California, Berkeley.