03327nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001600167072002300183072001500206100008000221245015200301250001800453264007500471300006500546336002600611337002600637338003600663347002400699490004700723505054400770520105601314650001602370650001402386650003602400650001902436700007702455700007602532710003402608773002602642776003602668776003602704776003602740830004702776856004602823978-3-031-62427-8DE-He21320251030113043.0cr nn 008mamaa240915s2024    sz |    s    |||| 0|eng d  a97830316242787 a10.1007/978-3-031-62427-82doi 4aQA276-280 7aPBT2bicssc 7aMAT0290002bisacsh 7aPBT2thema1 aWilson, Jeffrey R.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aModeling Binary Correlated Responsesh[electronic resource] :bUsing SAS, SPSS, R and STATA /cby Jeffrey R. Wilson, Kent A. Lorenz, Lori P. Selby.  a2nd ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXXV, 282 p. 28 illus., 21 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aICSA Book Series in Statistics,x2199-09990 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.  aThis book is an updated edition of Modeling Binary Correlated Responses Using SAS, SPSS and R, and now it includes the use of STATA. It uses these Statistical tools to analyze correlated binary data, accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages, as well as showcase both traditional and new methods for application to health-related research. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Short tutorials are in the appendix, for readers interested in learning more about the languages. 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, SPSS and STATA, allows for easy implementation by readers. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful. 0aStatistics. 0aBiometry.14aStatistical Theory and Methods.24aBiostatistics.1 aLorenz, Kent A.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aSelby, Lori P.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303162426108iPrinted edition:z978303162428508iPrinted edition:z9783031624292 0aICSA Book Series in Statistics,x2199-099940uhttps://doi.org/10.1007/978-3-031-62427-8