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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 : 978-1-58488-425-5 Langues : Anglais ( eng)Mots-clÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : Books on regression and the analysis of variance abound-many 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: 978-1-58488-425-5

Langues : Anglais (eng)

Mots-clÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : Books on regression and the analysis of variance abound-many 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)

Code-barres Cote Support Localisation Section DisponibilitÃ© 68137 FAR_11_68137 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043Extending 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 : 978-1-58488-424-8 Langues : Anglais ( eng)Mots-clÃ©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 well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level 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: 978-1-58488-424-8

Langues : Anglais (eng)

Mots-clÃ©s : 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 well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught. En ligne : http://books.google.fr/books?id=ODcRsWpGji4C&dq=extending+the+linear+model+with+ [...] ## Exemplaires (1)

Code-barres Cote Support Localisation Section DisponibilitÃ© 68106 FAR_11_68106 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043Generalized linear models and extensions / Hardin, J.W. ; Hilbe, J.M. (2007)

Titre : Generalized linear models and extensions Type de document : livre Auteurs : Hardin, J.W. ; Hilbe, J.M. Mention d'Ã©dition : 02 Ã©d. Editeur : College Station, Texas : Stata Press AnnÃ©e de publication : 2007 Importance : 387 p. ISBN/ISSN/EAN : 978-1-59718-014-6 Langues : Anglais ( eng)Mots-clÃ©s : Linear models Linear models (Statistics) Statistics Note de contenu : Pbk; Generalized linear models and extensions [livre] / Hardin, J.W. ; Hilbe, J.M. . - 02 Ã©d. . - College Station, Texas : Stata Press, 2007 . - 387 p.ISBN: 978-1-59718-014-6

Langues : Anglais (eng)

Mots-clÃ©s : Linear models Linear models (Statistics) Statistics Note de contenu : Pbk; ## Exemplaires (1)

Code-barres Cote Support Localisation Section DisponibilitÃ© 68865 Har_11_68865 Livre Salle des ouvrages 11_Mathématiques Disponible

Titre : An introduction to generalized linear models Type de document : livre Auteurs : DOBSON, A.J. Mention d'Ã©dition : 02 Ã©d. Editeur : Boca Raton, Florida : Chapman & Hall/CRC AnnÃ©e de publication : 2002 Importance : 225 p. ISBN/ISSN/EAN : 978-1-58488-165-0 Langues : Anglais ( eng)Mots-clÃ©s : Linear models Model Statistics En ligne : http://books.google.com/books?id=0CAgx5kQSwcC&printsec=frontcover&dq=An+introduc [...] An introduction to generalized linear models [livre] / DOBSON, A.J. . - 02 Ã©d. . - Boca Raton, Florida : Chapman & Hall/CRC, 2002 . - 225 p.ISBN: 978-1-58488-165-0

Langues : Anglais (eng)

Mots-clÃ©s : Linear models Model Statistics En ligne : http://books.google.com/books?id=0CAgx5kQSwcC&printsec=frontcover&dq=An+introduc [...] ## Exemplaires (1)

Code-barres Cote Support Localisation Section DisponibilitÃ© 66605 DOB_11_66605 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043

Titre : Categorical data analysis Type de document : livre Auteurs : Alan Agresti, Auteur Mention d'Ã©dition : 2nd ed. Editeur : Hoboken, New Jersey, USA : Wiley AnnÃ©e de publication : 2002 Collection : Wiley series in probability and statistics Importance : 710 p. ISBN/ISSN/EAN : 978-0-471-36093-3 Note gÃ©nÃ©rale : Voir aussi 1ère édition de 1990 à la cote 66555/11; DOI:10.1002/0471249688 Langues : Anglais ( eng)Mots-clÃ©s : Analysis Linear models Modelling Multivariate analysis Statistical methods RÃ©sumÃ© : The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis. Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of: * Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects * Stronger emphasis on logistic regression modeling of binary and multicategory data * An appendix showing the use of SAS for conducting nearly all analyses in the book * Prescriptions for how ordinal variables should be treated differently than nominal variables * Discussion of exact small-sample procedures * More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises. En ligne : http://dx.doi.org/10.1002/0471249688 Categorical data analysis [livre] / Alan Agresti, Auteur . - 2nd ed. . - Hoboken, New Jersey, USA : Wiley, 2002 . - 710 p.. - (Wiley series in probability and statistics) .ISBN: 978-0-471-36093-3

Voir aussi 1ère édition de 1990 à la cote 66555/11; DOI:10.1002/0471249688

Langues : Anglais (eng)

Mots-clÃ©s : Analysis Linear models Modelling Multivariate analysis Statistical methods RÃ©sumÃ© : The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis. Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of: * Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects * Stronger emphasis on logistic regression modeling of binary and multicategory data * An appendix showing the use of SAS for conducting nearly all analyses in the book * Prescriptions for how ordinal variables should be treated differently than nominal variables * Discussion of exact small-sample procedures * More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises. En ligne : http://dx.doi.org/10.1002/0471249688 ## Exemplaires (1)

Code-barres Cote Support Localisation Section DisponibilitÃ© 67581 AGR_11_67581 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043PermalinkPermalink