000 02122 a2200229 4500
008 140323b2008 xxu||||| |||| 00| 0 eng d
020 _a9781584889502
082 _a519.5
100 _aDobson, Annette J.
_956350
245 _aAn introduction to generalized linear models
_cDobson, Annette J.
250 _a3rd ed.
260 _aBoca Raton
_bCRC Press
_c2008
_9122501
300 _a307 p.
365 _bUKP 26.99
440 _aTexts in statistical science series
_9134911
520 _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)
650 _aLinear models (Statistics)
_943688
700 _aBarnett, Adrian G.
_956356
942 _cBK
952 _w2010-04-19
_A26.99
_BUKP
_C09/01/2009
_DHimanshu Book Co.
_E280704
_F31/12/2008
_G98126
_H31/12/2008
_IApproval
_J2137.60
_K20.00%
_L0.00
_MProf. Tejas Desai
_N29/12/2008
_p167020
_v2137.60
_r2010-04-19
_40
_00
_bVSL
_10
_o519.5 D6I6
_d2010-04-18
_70
_2ddc
_yBK
_aVSL
999 _c161640
_d161640