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Download the fantastic book titled Extending the Linear Model with R written by Julian J. Faraway, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Extending the Linear Model with R", which was released on 23 March 2016. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Mathematics genre.

Summary of Extending the Linear Model with R by Julian J. Faraway PDF

Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics. New to the Second Edition Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available Updated coverage of splines and confidence bands in the chapter on nonparametric regression New material on random forests for regression and classification Revamped R code throughout, particularly the many plots using the ggplot2 package Revised and expanded exercises with solutions now included Demonstrates the Interplay of Theory and Practice This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.


Detail About Extending the Linear Model with R PDF

  • Author : Julian J. Faraway
  • Publisher : CRC Press
  • Genre : Mathematics
  • Total Pages : 399 pages
  • ISBN : 1498720986
  • PDF File Size : 24,7 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Extending the Linear Model with R

Extending the Linear Model with R
  • Publisher : CRC Press
  • File Size : 32,9 Mb
  • Release Date : 23 March 2016
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Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages

Linear Models with R

Linear Models with R
  • Publisher : CRC Press
  • File Size : 29,8 Mb
  • Release Date : 19 April 2016
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A Hands-On Way to Learning Data AnalysisPart 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 with R, Second Edition

Linear Models with R, Second Edition
  • Publisher : CRC Press
  • File Size : 25,6 Mb
  • Release Date : 01 July 2014
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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.

Extending the Linear Model with R

Extending the Linear Model with R
  • Publisher : CRC Press
  • File Size : 54,8 Mb
  • Release Date : 10 February 2016
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Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined

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Dynamic Linear Models with R
  • Publisher : Springer Science & Business Media
  • File Size : 54,6 Mb
  • Release Date : 12 June 2009
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State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this

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Introduction to General and Generalized Linear Models
  • Publisher : CRC Press
  • File Size : 31,7 Mb
  • Release Date : 09 November 2010
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Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data.

A Primer on Linear Models

A Primer on Linear Models
  • Publisher : CRC Press
  • File Size : 26,8 Mb
  • Release Date : 31 March 2008
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A Primer on Linear Models presents a unified, thorough, and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these

Generalized Linear Mixed Models

Generalized Linear Mixed Models
  • Publisher : CRC Press
  • File Size : 21,6 Mb
  • Release Date : 19 April 2016
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With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to

Advanced Linear Models

Advanced Linear Models
  • Publisher : Routledge
  • File Size : 53,9 Mb
  • Release Date : 04 May 2018
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This work details the statistical inference of linear models including parameter estimation, hypothesis testing, confidence intervals, and prediction. The authors discuss the application of statistical theories and methodologies to various

Interpretable Machine Learning

Interpretable Machine Learning
  • Publisher : Lulu.com
  • File Size : 39,7 Mb
  • Release Date : 25 February 2024
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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision