LEADER 04485nam 22006255 450 001 9910999676303321 005 20260204151819.0 010 $a9783031690389 010 $a3031690389 024 7 $a10.1007/978-3-031-69038-9 035 $a(MiAaPQ)EBC32011552 035 $a(Au-PeEL)EBL32011552 035 $a(CKB)38463807200041 035 $a(DE-He213)978-3-031-69038-9 035 $a(EXLCZ)9938463807200041 100 $a20250417d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLog-Linear Models and Logistic Regression /$fby Ronald Christensen 205 $a3rd ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (814 pages) 225 1 $aSpringer Texts in Statistics,$x2197-4136 311 08$a9783031690372 311 08$a3031690370 327 $aTwo-Dimensional Tables and Simple Logistic Regression -- Three-Dimensional Tables -- Logistic Regression, Logit Models, and Logistic Discrimination -- Independence Relationships and Graphical Models -- Model Selection Methods and Model Evaluation -- Models for Factors with Quantitative Levels -- Fixed and Random Zeros -- Generalized Linear Models -- The Matrix Approach to Log-Linear Models -- The Matrix Approach to Logit Models -- Maximum Likelihood Theory for Log-Linear Models -- Bayesian Binomial Regression. Exact Conditional Tests. - Correspondence Analysis. 330 $aThis book examines statistical models for frequency data. The primary focus is on log-linear models for contingency tables but also includes extensive discussion of logistic regression. Topics such as logistic discrimination, generalized linear models, and correspondence analysis are also explored. The treatment is designed for readers with prior knowledge of analysis of variance and regression. It builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. While emphasizing similarities between methods for discrete and continuous data, this book also carefully examines the differences in model interpretations and evaluation that occur due to the discrete nature of the data. Numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. A major addition to the third edition is the availability of a companion online manual providing R code for the procedures illustrated in the book. The book begins with an extensive discussion of odds and odds ratios as well as concrete illustrations of basic independence models for contingency tables. After developing a sound applied and theoretical basis for frequency models analogous to ANOVA and regression, the book presents, for contingency tables, detailed discussions of the use of graphical models, of model selection procedures, and of models with quantitative factors. It then explores generalized linear models, after which all the fundamental results are reexamined using powerful matrix methods. The book then gives an extensive treatment of Bayesian procedures for analyzing logistic regression and other regression models for binomial data. Bayesian methods are conceptually simple and unlike traditional methods allow accurate conclusions to be drawn without requiring large sample sizes. The book concludes with two new chapters: one on exact conditional tests for small sample sizes and another on the graphical procedure known as correspondence analysis. 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aRegression analysis 606 $aStatistics 606 $aBiometry 606 $aLinear Models and Regression 606 $aBayesian Inference 606 $aBiostatistics 606 $aModels lineals (Estadística)$2thub 608 $aLlibres electrònics$2thub 615 0$aRegression analysis. 615 0$aStatistics. 615 0$aBiometry. 615 14$aLinear Models and Regression. 615 24$aBayesian Inference. 615 24$aBiostatistics. 615 7$aModels lineals (Estadística) 676 $a519.5/35 700 $aChristensen$b Ronald$066381 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910999676303321 996 $aLog-linear models$9435077 997 $aUNINA