國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Generalized linear models with examp...

Dunn, Peter K.

Generalized linear models with examples in R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Generalized linear models with examples in Rby Peter K. Dunn, Gordon K. Smyth.
作者:	Dunn, Peter K.
其他作者:	Smyth, Gordon K.
出版者:	New York, NY :Springer New York :2018.
面頁冊數:	xx, 562 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Linear models (Statistics)
電子資源:	https://doi.org/10.1007/978-1-4419-0118-7
ISBN:	9781441901187$q(electronic bk.)

Generalized linear models with examples in R
Dunn, Peter K.

Generalized linear models with examples in R[electronic resource] /by Peter K. Dunn, Gordon K. Smyth. - New York, NY :Springer New York :2018. - xx, 562 p. :ill., digital ;24 cm. - Springer texts in statistics,1431-875X. - Springer texts in statistics..

Statistical models -- Linear regression models -- Linear regression models: diagnostics and model-building -- Beyond linear regression: the method of maximum likelihood -- Generalized linear models: structure -- Generalized linear models: estimation -- Generalized linear models: inference -- Generalized linear models: diagnostics -- Models for proportions: binomial GLMs -- Models for counts: Poisson and negative binomial GLMs -- Positive continuous data: gamma and inverse Gaussian GLMs -- Tweedie GLMs -- Extra problems -- Appendix A: Using R for data analysis -- Appendix B: The GLMsData package -- Index: Data sets -- Index: R commands -- Index: General Topics.

This textbook presents an introduction to multiple linear regression, providing real-world data sets and practice problems. A practical working knowledge of applied statistical practice is developed through the use of these data sets and numerous case studies. The authors include a set of practice problems both at the end of each chapter and at the end of the book. Each example in the text is cross-referenced with the relevant data set, so that readers can load the data and follow the analysis in their own R sessions. The balance between theory and practice is evident in the list of problems, which vary in difficulty and purpose. This book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples. Focusing on the connections between generalized linear models (GLMs) and linear regression, the book also references advanced topics and tools that have not typically been included in introductions to GLMs to date, such as Tweedie family distributions with power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, and randomized quantile residuals. In addition, the authors introduce the new R code package, GLMsData, created specifically for this book. Generalized Linear Models with Examples in R balances theory with practice, making it ideal for both introductory and graduate-level students who have a basic knowledge of matrix algebra, calculus, and statistics.

ISBN: 9781441901187$q(electronic bk.)

Standard No.: 10.1007/978-1-4419-0118-7doiSubjects--Topical Terms:

181866
Linear models (Statistics)

LC Class. No.: QA279 / .D866 2018

Dewey Class. No.: 519.5

Generalized linear models with examples in R
LDR:03219nmm a2200337 a 4500 001 547368
003 DE-He213
005 20190503114540.0
006 m d
007 cr nn 008maaau
008 190709s2018 nyu s 0 eng d
020 $a 9781441901187$q(electronic bk.)
020 $a 9781441901170$q(paper)
024 7 $a 10.1007/978-1-4419-0118-7 $2 doi
035 $a 978-1-4419-0118-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279 $b .D866 2018
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA279 $b .D923 2018
100 1 $a Dunn, Peter K. $3 826626
245 1 0 $a Generalized linear models with examples in R $h [electronic resource] / $c by Peter K. Dunn, Gordon K. Smyth.
260 $a New York, NY : $b Springer New York : $b Imprint: Springer, $c 2018.
300 $a xx, 562 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 1431-875X
505 0 $a Statistical models -- Linear regression models -- Linear regression models: diagnostics and model-building -- Beyond linear regression: the method of maximum likelihood -- Generalized linear models: structure -- Generalized linear models: estimation -- Generalized linear models: inference -- Generalized linear models: diagnostics -- Models for proportions: binomial GLMs -- Models for counts: Poisson and negative binomial GLMs -- Positive continuous data: gamma and inverse Gaussian GLMs -- Tweedie GLMs -- Extra problems -- Appendix A: Using R for data analysis -- Appendix B: The GLMsData package -- Index: Data sets -- Index: R commands -- Index: General Topics.
520 $a This textbook presents an introduction to multiple linear regression, providing real-world data sets and practice problems. A practical working knowledge of applied statistical practice is developed through the use of these data sets and numerous case studies. The authors include a set of practice problems both at the end of each chapter and at the end of the book. Each example in the text is cross-referenced with the relevant data set, so that readers can load the data and follow the analysis in their own R sessions. The balance between theory and practice is evident in the list of problems, which vary in difficulty and purpose. This book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples. Focusing on the connections between generalized linear models (GLMs) and linear regression, the book also references advanced topics and tools that have not typically been included in introductions to GLMs to date, such as Tweedie family distributions with power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, and randomized quantile residuals. In addition, the authors introduce the new R code package, GLMsData, created specifically for this book. Generalized Linear Models with Examples in R balances theory with practice, making it ideal for both introductory and graduate-level students who have a basic knowledge of matrix algebra, calculus, and statistics.
650 0 $a Linear models (Statistics) $3 181866
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
700 1 $a Smyth, Gordon K. $3 826627
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer texts in statistics. $3 559235
856 4 0 $u https://doi.org/10.1007/978-1-4419-0118-7
950 $a Mathematics and Statistics (Springer-11649)