Categorical Data Analysis by Alan Agresti

By Alan Agresti

Praise for the second one Edition

"A must-have e-book for a person awaiting to do study and/or purposes in express facts analysis."
Statistics in Medicine

"It is a complete satisfaction interpreting this book."
Pharmaceutical Research

"If you do any research of express information, this can be an important computing device reference."

The use of statistical tools for examining express information has elevated dramatically, relatively within the biomedical, social sciences, and monetary industries. Responding to new advancements, this ebook deals a finished remedy of crucial tools for express facts analysis.

Categorical facts research, 3rd Edition summarizes the newest equipment for univariate and correlated multivariate specific responses. Readers will discover a unified generalized linear types process that connects logistic regression and Poisson and damaging binomial loglinear types for discrete facts with general regression for non-stop information. This variation additionally features:

  • An emphasis on logistic and probit regression tools for binary, ordinal, and nominal responses for self sufficient observations and for clustered facts with marginal versions and random results models
  • Two new chapters on replacement equipment for binary reaction info, together with smoothing and regularization tools, type equipment akin to linear discriminant research and class bushes, and cluster analysis
  • New sections introducing the Bayesian process for ways in that chapter
  • More than a hundred analyses of knowledge units and over six hundred exercises
  • Notes on the finish of every bankruptcy that supply references to contemporary learn and issues no longer coated within the textual content, associated with a bibliography of greater than 1,200 sources
  • A supplementary web site displaying the way to use R and SAS; for all examples within the textual content, with info additionally approximately SPSS and Stata and with workout solutions

Categorical info research, 3rd Edition is a useful instrument for statisticians and methodologists, similar to biostatisticians and researchers within the social and behavioral sciences, drugs and public healthiness, advertising, schooling, finance, organic and agricultural sciences, and commercial caliber control.

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Extra info for Categorical Data Analysis

Sample text

Then the odds ratio is ␪s ␲ 11 r␲ 12 ␲ 21 r␲ 22 s ␲ 11␲ 22 ␲ 12 ␲ 21 . 5 . An alternative name for ␪ is the cross-product ratio, since it equals the ratio of the products ␲ 11␲ 22 and ␲ 12 ␲ 21 of probabilities from diagonally opposite cells ŽYule 1900, 1912.. 4 Properties of the Odds Ratio The odds ratio can equal any nonnegative number. The condition ⍀ 1 s ⍀ 2 and hence Žwhen all cell probabilities are positive. ␪ s 1 corresponds to independence of X and Y. When 1 - ␪ - ϱ, subjects in row 1 are more likely to have a success than are subjects in row 2; that is, ␲ 1 ) ␲ 2 .

The score method does not use ␤ˆ. Thus, when ␤ is a model parameter, compute the score statistic for testing H0 : ␤ s ␤ 0 without fitting the advantageous when fitting several models in an exploratory analysis and computationally intensive. An advantage of the score and likelihood-ratio one can usually model. This is model fitting is methods is that 27 PROBLEMS they apply even when < ␤ˆ < s ϱ. In that case, one cannot compute the Wald statistic. Another disadvantage of the Wald method is that its results depend on the parameterization; inference based on ␤ˆ and its SE is not equivalent to inference based on a nonlinear function of it, such as log ␤ˆ and its SE.

When dim Ž ␪ . s p, the true df s Ž c y 1. 3.. 2.. We now show a goodness-to-fit test with estimated expected frequencies. A sample of 156 dairy calves born in Okeechobee County, Florida, were classified according to whether they caught pneumonia within 60 days of birth. Calves that got a pneumonia infection were also classified according to whether they got a secondary infection within 2 weeks after the first infection cleared up. 1 shows the data. Calves that did not get a primary infection could not get a secondary infection, so no observations can fall in the category for ‘‘no’’ primary infection and ‘‘ yes’’ secondary infection.

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