LEADER 03950nam 22005415 450 001 9910300108303321 005 20250505001244.0 010 $a1-4419-0118-3 024 7 $a10.1007/978-1-4419-0118-7 035 $a(CKB)4100000007127540 035 $a(DE-He213)978-1-4419-0118-7 035 $a(MiAaPQ)EBC6311738 035 $a(PPN)232468303 035 $a(EXLCZ)994100000007127540 100 $a20181110d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGeneralized Linear Models With Examples in R /$fby Peter K. Dunn, Gordon K. Smyth 205 $a1st ed. 2018. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2018. 215 $a1 online resource (XX, 562 p. 115 illus.) 225 1 $aSpringer Texts in Statistics,$x2197-4136 300 $aIncludes index. 311 08$a1-4419-0117-5 327 $aStatistical 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. . 330 $aThis textbook presents an introduction to multiple linear regression, providing real-world data sets and practice problems. A practical working knowledge of applied statistical practice is developed through the use of these data sets and numerous case studies. The authors include a set of practice problems both at the end of each chapter and at the end of the book. Each example in the text is cross-referenced with the relevant data set, so that readers can load the data and follow the analysis in their own R sessions. The balance between theory and practice is evident in the list of problems, which vary in difficulty and purpose. This book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples. Focusing on the connections between generalized linear models (GLMs) and linear regression, the book also references advanced topics and tools that have not typically been included in introductions to GLMs to date, such as Tweedie family distributions with power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, and randomized quantile residuals. In addition, the authors introduce the new R code package, GLMsData, created specifically for this book. Generalized Linear Models with Examples in R balances theory with practice, making it ideal for both introductory and graduate-level students who have a basic knowledge of matrix algebra, calculus, and statistics. . 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 676 $a519.5 700 $aDunn$b Peter K$4aut$4http://id.loc.gov/vocabulary/relators/aut$0220757 702 $aSmyth$b Gordon K$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910300108303321 996 $aGeneralized Linear Models With Examples in R$92057007 997 $aUNINA