000 | 02122 a2200229 4500 | ||
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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 |
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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 |
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999 |
_c161640 _d161640 |