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Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models / Faraway, J.J. (2006)
Titre : Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models Type de document : livre Auteurs : Faraway, J.J., Auteur Editeur : Boca Raton : Chapman & Hall/CRC AnnÃ©e de publication : 2006 Collection : Texts in statistical science Importance : 301 p. ISBN/ISSN/EAN : 9781584884248 Langues : Anglais (eng) MotsclÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : 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 regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site (www.stat.lsa.umich.edu/~faraway/ELM) holds all of the data described in the book. Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a wellstocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduatelevel courses in this area are taught. En ligne : http://books.google.fr/books?id=ODcRsWpGji4C&dq=extending+the+linear+model+with+ [...] Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models [livre] / Faraway, J.J., Auteur .  Boca Raton : Chapman & Hall/CRC, 2006 .  301 p..  (Texts in statistical science) .
ISBN : 9781584884248
Langues : Anglais (eng)
MotsclÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : 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 regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site (www.stat.lsa.umich.edu/~faraway/ELM) holds all of the data described in the book. Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a wellstocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduatelevel courses in this area are taught. En ligne : http://books.google.fr/books?id=ODcRsWpGji4C&dq=extending+the+linear+model+with+ [...] Exemplaires (1)
Codebarres Cote Support Localisation Section DisponibilitÃ© 68106 FAR_11_68106 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043
Titre : Linear models with R Type de document : livre Auteurs : Faraway, J.J. Editeur : Boca Raton : Chapman & Hall/CRC AnnÃ©e de publication : 2005 Collection : Texts in statistical science Importance : 229 p. ISBN/ISSN/EAN : 9781584884255 Langues : Anglais (eng) MotsclÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : Books on regression and the analysis of variance aboundmany are introductory, many are theoretical. While most of them do serve a purpose, the fact remains that data analysis cannot be properly learned without actually doing it, and this means using a statistical software package. There are many of these to choose from as well, all with their particular strengths and weaknesses. Lately, however, one such package has begun to rise above the others thanks to its free availability, its versatility as a programming language, and its interactivity. That software is R. In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from http://www.stat.lsa.umich.edu/~faraway/LMR/. The author assumes that readers know the essentials of statistical inference and have a basic knowledge of data analysis, linear algebra, and calculus. The treatment reflects his view of statistical theory and his belief that qualitative statistical concepts, while somewhat more difficult to learn, are just as important because they enable us to practice statistics rather than just talk about it. [RÃ©sumÃ© Ã©diteur] En ligne : http://books.google.fr/books?id=fvenzpofkagC&dq=linear+models+with+r&pg=PP1&ots= [...] Linear models with R [livre] / Faraway, J.J. .  Boca Raton : Chapman & Hall/CRC, 2005 .  229 p..  (Texts in statistical science) .
ISBN : 9781584884255
Langues : Anglais (eng)
MotsclÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : Books on regression and the analysis of variance aboundmany are introductory, many are theoretical. While most of them do serve a purpose, the fact remains that data analysis cannot be properly learned without actually doing it, and this means using a statistical software package. There are many of these to choose from as well, all with their particular strengths and weaknesses. Lately, however, one such package has begun to rise above the others thanks to its free availability, its versatility as a programming language, and its interactivity. That software is R. In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from http://www.stat.lsa.umich.edu/~faraway/LMR/. The author assumes that readers know the essentials of statistical inference and have a basic knowledge of data analysis, linear algebra, and calculus. The treatment reflects his view of statistical theory and his belief that qualitative statistical concepts, while somewhat more difficult to learn, are just as important because they enable us to practice statistics rather than just talk about it. [RÃ©sumÃ© Ã©diteur] En ligne : http://books.google.fr/books?id=fvenzpofkagC&dq=linear+models+with+r&pg=PP1&ots= [...] Exemplaires (1)
Codebarres Cote Support Localisation Section DisponibilitÃ© 68137 FAR_11_68137 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043