LEADER 01507nam2-2200457---450- 001 990000868290403321 005 20140124100317.0 010 $a0-387-90642-8 010 $a0-387-90667-3 010 $a0-387-95174-1 010 $a0-387-95175-X 035 $a000086829 035 $aFED01000086829 035 $a(Aleph)000086829FED01 035 $a000086829 100 $a20020821d1982----km-y0itay50------ba 101 0 $aeng 102 $aUS 105 $ay---a---001yy 200 1 $a<<1.: The >>quantum theory of Planck, Einstein, Bohr and Sommerfeld: its foundation and the rise of its difficulties, 1900-1925 210 $aNew York [etc.]$cSpringer$dc1982 215 $a2 v. (xlvii, vi, 878 p.)$d24 cm 461 0$1001000083413$12001$a<>historical development of quantum theory$v01 610 0 $aMeccanica quantistica 676 $a530.1 700 1$aMehra,$bJagdish$040832 701 1$aRechenberg,$bHelmut$040833 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000868290403321 952 $a02 50 F 12$b4627$fFINBN 952 $a02 50 F 11$b3686$fFINBN 952 $a22-155$b12188$fFI1 952 $a22-155.001$b12154$fFI1 952 $a22-155-A$bDIPFIS 131$fFI1 952 $a22-155.001-A$bDIPFIS 132$fFI1 952 $a110-G-10-(1,2)$b833-834$fMA1 959 $aFINBN 959 $aMA1 996 $aQuantum theory of Planck, Einstein, Bohr and Sommerfeld: its foundation and the rise of its difficulties, 1900-1925$9349332 997 $aUNINA LEADER 03952nam 22005415 450 001 9910300108303321 005 20250916143103.0 010 $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