03733oam 2200697I 450 991096166510332120251116235312.097810400691271040069126978042914913904291491319781420075779142007577210.1201/9781420075779(CKB)3710000000391508(EBL)1633158(SSID)ssj0001459331(PQKBManifestationID)12616293(PQKBTitleCode)TC0001459331(PQKBWorkID)11457378(PQKB)10829678(Au-PeEL)EBL1633158(CaPaEBR)ebr11167286(OCoLC)908078815(OCoLC)1000427446(FINmELB)ELB140801(MiAaPQ)EBC1633158(EXLCZ)99371000000039150820180706d2009 uy 0engur|n|---|||||txtccrLogistic regression models /Joseph M. Hilbe1st ed.Boca Raton :Chapman & Hall/CRC,2009.1 online resource (658 p.)Chapman & Hall/CRC texts in Statistical Science SeriesA Chapman & Hall BookDescription based upon print version of record.9781138106710 1138106712 9781420075755 1420075756 Includes bibliographical references and index.Front cover; Contents; Preface; Chapter 1. Introduction; Chapter 2. Concepts Related to the Logistic Model; Chapter 3. Estimation Methods; Chapter 4. Derivation of the Binary Logistic Algorithm; Chapter 5. Model Development; Chapter 6. Inteactions; Chapter 7. Analysis of Model Fit; Chapter 8. Binomial Logistic Regression; Chapter 9. Overdispersion; Chapter 10. Ordered Logistic Regression; Chapter 11. Multinomial Logistic Regression; Chapter 12. Alternative Categorical Response Models; Chapter 13. Panel Models; Chapter 14. Other Types of Logistic-Based ModelsChapter 15. Exact Logistic RegressionConclusion; Appendix A: Brief Guide to Using Stata Commands; Appendix B: Stata and R Logistic Models; Appendix C: Greek Letters and Major Functions; Appendix D: Stata Binary Logistic Command; Appendix E: Derivation of the Beta Binomial; Appendix F: Likelihood Function of the Adaptive Gauss-Hermite Quadrature Method of Estimation; Appendix G: Data Sets; Appendix H: Marginal Effects and Discrete Change ; References; Author Index; Subject Index; Back coverThis text presents an overview of the full range of logistic models, including binary, proportional, ordered, and categorical response regression procedures. It illustrates how to apply the models to medical, health, environmental/ecological, physical, and social science data. Stata is used to develop, evaluate, and display most models while R code is given at the end of most chapters. The author examines the theoretical foundation of the models and describes how each type of model is established, interpreted, and evaluated as to its goodness of fit. Example data sets are available online in various formats and a solutions manual is available upon qualifying course adoption.Texts in statistical science.Logistic regression analysisData processingLogistic regression analysisData processing.519.5/36Hilbe Joseph M.1944-281747MiAaPQMiAaPQMiAaPQBOOK9910961665103321Logistic regression models70080UNINA