---

＜E-Book＞
Generalized Linear Models With Examples in R / by Peter K. Dunn, Gordon K. Smyth
(Springer Texts in Statistics. ISSN:21974136)

Edition	1st ed. 2018.
Publisher	(New York, NY : Springer New York : Imprint: Springer)
Year	2018
Size	XX, 562 p. 115 illus : online resource
Authors	*Dunn, Peter K author Smyth, Gordon K author SpringerLink (Online service)
Subjects	LCSH:Statistics  LCSH:Mathematical statistics—Data processing FREE:Statistical Theory and Methods FREE:Statistics and Computing
Notes	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. HTTP:URL=https://doi.org/10.1007/978-1-4419-0118-7

 Similar Items