02149 a2200217 4500008004100000020001800041082001000059100003000069245006900099250001200168260004000180300001200220365001400232440004800246520121600294650003801510700003001548942000701578999001901585952032701604140323b2008 xxu||||| |||| 00| 0 eng d a9781584889502 a519.5 aDobson, Annette J.956350 aAn introduction to generalized linear modelscDobson, Annette J. a3rd ed. aBoca RatonbCRC Pressc20089122501 a307 p. bUKP 26.99 aTexts in statistical science series9134911 aContinuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered. (Source: www.amazon.com) aLinear models (Statistics)943688 aBarnett, Adrian G.956356 cBK c161640d161640 00102ddc4070aVSLbVSLcSlot 1412 (0 Floor, East Wing)d2010-04-18kSlot 1412 (0 Floor, East Wing)l1m3o519.5 D6I6p167020r2014-03-13s2013-02-27v2137.60w2010-04-19yBKA26.99BUKPC09/01/2009DHimanshu Book Co.E280704F31/12/2008G98126H31/12/2008IApprovalJ2137.60K20.00%L0.00MProf. Tejas DesaiN29/12/2008