LEADER 03677nam 22006255 450 001 9910734892503321 005 20250413132354.0 010 $a3-319-23805-1 024 7 $a10.1007/978-3-319-23805-0 035 $a(CKB)3710000000492401 035 $a(EBL)4178556 035 $a(SSID)ssj0001585332 035 $a(PQKBManifestationID)16265495 035 $a(PQKBTitleCode)TC0001585332 035 $a(PQKBWorkID)14864397 035 $a(PQKB)10525865 035 $a(DE-He213)978-3-319-23805-0 035 $a(MiAaPQ)EBC4178556 035 $a(PPN)190527080 035 $a(EXLCZ)993710000000492401 100 $a20151012d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModeling Binary Correlated Responses using SAS, SPSS and R /$fby Jeffrey R. Wilson, Kent A. Lorenz 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (281 p.) 225 1 $aICSA Book Series in Statistics,$x2199-0999 ;$v9 300 $aDescription based upon print version of record. 311 08$a3-319-23804-3 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction to Binary logistic Regression -- Growth of the Logistic Regression Model -- Standard Binary Logistic Regression Model -- Overdispersed Logistic Regression Model -- Weighted Logistic Regression Model -- Generalized Estimating Equations Logistic Regression -- Generalized Method of Moments logistic regression Model -- Exact Logistic Regression Model -- Two-Level Nested Logistic Regression Model -- Hierarchical Logistic Regression models -- Fixed Effects Logistic Regression Model -- Heteroscedastic Logistic Regression Model. 330 $aStatistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects.  Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful. 410 0$aICSA Book Series in Statistics,$x2199-0999 ;$v9 606 $aStatistics 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aBiostatistics 615 0$aStatistics. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 676 $a519.5 700 $aWilson$b Jeffrey R$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755664 702 $aLorenz$b Kent A$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734892503321 996 $aModeling binary correlated responses using SAS, SPSS and R$93403797 997 $aUNINA