Download Categorical Data Analysis, Second Edition by Alan Agresti PDF

By Alan Agresti

Amstat News requested 3 assessment editors to cost their most sensible 5 favourite books within the September 2003 factor. Categorical info Analysis used to be between these selected.

A helpful re-creation of a regular reference

"A 'must-have' e-book for a person watching for to do study and/or purposes in specific facts analysis."
-Statistics in drugs on specific facts Analysis, First variation

The use of statistical equipment for express facts has elevated dramatically, quite for functions within the biomedical and social sciences. Responding to new advancements within the box in addition to to the desires of a brand new iteration of execs and scholars, this re-creation of the vintage Categorical facts Analysis deals a entire advent to crucial equipment for express information research.

Designed for statisticians and biostatisticians in addition to scientists and graduate scholars practising information, Categorical info Analysis, moment variation summarizes the most recent tools for univariate and correlated multivariate specific responses. Readers will discover a unified generalized linear types method that connects logistic regression and Poisson and unfavorable binomial regression for discrete facts with basic regression for non-stop facts. including to the worth within the re-creation is assurance of:

  • 3 new chapters on equipment for repeated dimension and different kinds of clustered express info, together with marginal types and linked generalized estimating equations (GEE) tools, and combined types with random results content material:
    Chapter 1 creation: Distributions and Inference for express info (pages 1–35):
    Chapter 2 Describing Contingency Tables (pages 36–69):
    Chapter three Inference for Contingency Tables (pages 70–114):
    Chapter four creation to Generalized Linear versions (pages 115–164):
    Chapter five Logistic Regression (pages 165–210):
    Chapter 6 construction and utilising Logistic Regression types (pages 211–266):
    Chapter 7 Logit types for Multinomial Responses (pages 267–313):
    Chapter eight Loglinear versions for Contingency Tables (pages 314–356):
    Chapter nine development and lengthening Loglinear/Logit types (pages 357–408):
    Chapter 10 versions for Matched Pairs (pages 409–454):
    Chapter eleven reading Repeated specific reaction facts (pages 455–490):
    Chapter 12 Random results: Generalized Linear combined types for express Responses (pages 491–537):
    Chapter thirteen different mix types for express facts (pages 538–575):
    Chapter 14 Asymptotic thought for Parametric types (pages 576–599):
    Chapter 15 replacement Estimation conception for Parametric versions (pages 600–618):
    Chapter sixteen old travel of express facts research (pages 619–631):

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Additional resources for Categorical Data Analysis, Second Edition

Example text

J To eliminate redundancies, we treat L as a function of Ž␲ 1 , . . , since ␲c s 1 y Ž␲ 1 q иии q␲cy1 .. Thus, Ѩ␲crѨ␲ j s y1, j s 1, . . , c y 1. Since Ѩ log ␲c Ѩ␲ j 1 Ѩ␲c s ␲c Ѩ␲ j 1 sy ␲c , differentiating LŽ ␲ . with respect to ␲ j gives the likelihood equation Ѩ LŽ ␲ . Ѩ␲ j s nj ␲j y nc ␲c s0. The ML solution satisfies ␲ ˆ jr␲ˆc s n jrn c . Now ␲ ˆc Ý ␲ˆ j s 1 s j žÝ / nj j nc s ␲ ˆc n nc , so ␲ ˆc s n crn and then ␲ˆ j s n jrn. , this solution does maximize the likelihood. Thus, the ML estimates of Ä␲ j 4 are the sample proportions.

6 . In fact, the odds ratio is equally valid for prospective, retrospective, or cross-sectional sampling designs. The sample odds ratio estimates the same parameter in each case. For cell counts Ä n i j 4 , the sample odds ratio is ␪ˆs n11 n 22 rn12 n 21 . This does not change when both cell counts within any row are multiplied by a nonzero constant or when both cell counts within any column are multiplied by a nonzero constant. An implication is that the sample odds ratio 46 DESCRIBING CONTINGENCY TABLES estimates the same characteristic Ž ␪ .

R2 differences Ä␲ j y ␲ k 4 Žsee Fitzpatrick and Scott 1987; Goodman 1965.. Categorical Data Analysis, Second Edition. Alan Agresti Copyright ¶ 2002 John Wiley & Sons, Inc. ISBN: 0-471-36093-7 CHAPTER 2 Describing Contingency Tables In this chapter we introduce tables that display relationships between categorical variables. We also define parameters that summarize their association. 2 are used to compare groups on the proportions of responses in the outcome categories. The odds ratio has special importance, appearing as a parameter in models discussed later.

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