An Introduction to Categorical Data Analysis (Wiley Series by Alan Agresti

By Alan Agresti

Praise for the 1st Edition
"This is a wonderful textual content from which to coach specific facts research, at various degrees. . . [t]his e-book should be very hugely recommended."
Short booklet Reviews
"Of nice curiosity to power readers is the range of fields which are represented within the examples: overall healthiness care, monetary, govt, product advertising, and activities, to call a few."
Journal of caliber Technology
"Alan Agresti has written one other excellent account of the research of specific data."
—The Statistician
The use of statistical tools for express information is ever expanding in today's international. An advent to express information research, moment Edition offers an utilized creation to an important tools for examining express info. This new version summarizes tools that experience lengthy performed a renowned position in info research, comparable to chi-squared checks, and likewise locations precise emphasis on logistic regression and different modeling thoughts for univariate and correlated multivariate specific responses.
This moment variation features:* new chapters at the tools for clustered information, with an emphasis on generalized estimating equations (GEE) and random results versions* A unified viewpoint according to generalized linear versions* An emphasis on logistic regression modeling* An appendix that demonstrates using SAS(r) for all tools* An exciting ancient standpoint at the improvement of the equipment* really expert tools for ordinal information, small samples, multicategory information, and coupled pairs* greater than a hundred analyses of genuine info units and approximately three hundred exercises
Written in an utilized, nontechnical variety, the ebook illustrates equipment utilizing a large choice of actual information, together with scientific scientific trials, drug use by way of childrens, basketball capturing, horseshoe crab mating, environmental evaluations, correlates of happiness, and masses more.
An creation to specific facts research, moment Edition is a useful instrument for social, behavioral, and biomedical scientists, in addition to researchers in public healthiness, advertising, schooling, organic and agricultural sciences, and business quality controls.

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Additional resources for An Introduction to Categorical Data Analysis (Wiley Series in Probability and Statistics)

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By contrast, the relative risk requires this, and its value also depends on whether it is applied to the first or to the second outcome category. When both variables are response variables, the odds ratio can be defined using joint probabilities as θ= π11 π22 π11 /π12 = π21 /π22 π12 π21 The odds ratio is also called the cross-product ratio, because it equals the ratio of the products π11 π22 and π12 π21 of cell probabilities from diagonally opposite cells. 4) For a multinomial distribution over the four cells or for independent binomial distributions for the two rows, this is the ML estimator of θ.

Because of this skewness, statistical inference for the odds ratio uses an alternative but equivalent measure – its natural logarithm, log(θ ). Independence corresponds to log(θ ) = 0. 0. 7. The log odds ratio is symmetric about zero, in the sense that reversing rows or reversing columns changes its sign. 7, represent the same strength of association. Doubling a log odds ratio corresponds to squaring an odds ratio. 25. ˆ has a less skewed sampling distribution that is The sample log odds ratio, log θ, bell-shaped.

The result of a diagnostic test is said to be positive if it states that the disease is present and negative if it states that the disease is absent. The accuracy of diagnostic tests is often assessed with two conditional probabilities: Given that a subject has the disease, the probability the diagnostic test is positive is called the sensitivity. Given that the subject does not have the disease, the probability the test is negative is called the specificity. Let X denote the true state of a person, with categories 1 = diseased, 2 = not diseased, and let Y = outcome of diagnostic test, with categories 1 = positive, 2 = negative.

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