By Alan Agresti

ISBN-10: 0471226181

ISBN-13: 9780471226185

**Praise for the 1st Edition**

"This is a wonderful textual content from which to educate specific info research, at various degrees. . . [t]his ebook should be very hugely recommended."

—*Short e-book Reviews*

"Of nice curiosity to power readers is the diversity of fields which are represented within the examples: healthiness care, monetary, executive, product advertising and marketing, and activities, to call a few."

—*Journal of caliber Technology*

"Alan Agresti has written one other marvelous account of the research of specific data."

—The Statistician

The use of statistical tools for specific information is ever expanding in today's global. *An advent to express information research, moment Edition* offers an utilized creation to an important equipment for interpreting express information. This re-creation summarizes equipment that experience lengthy performed a favorite position in info research, reminiscent of chi-squared checks, and likewise locations specific emphasis on logistic regression and different modeling recommendations for univariate and correlated multivariate specific responses.

This moment variation features:* new chapters at the equipment for clustered facts, with an emphasis on generalized estimating equations (GEE) and random results versions* A unified standpoint in response to generalized linear versions* An emphasis on logistic regression modeling* An appendix that demonstrates using SAS(r) for all tools* An interesting historic point of view at the improvement of the tools* really good tools for ordinal info, small samples, multicategory info, and coupled pairs* greater than a hundred analyses of actual facts units and approximately three hundred exercises

Written in an utilized, nontechnical variety, the publication illustrates equipment utilizing a wide selection of actual info, together with scientific scientific trials, drug use by way of little ones, basketball capturing, horseshoe crab mating, environmental evaluations, correlates of happiness, and masses more.

*An creation to specific info research, moment Edition* is a useful software for social, behavioral, and biomedical scientists, in addition to researchers in public health and wellbeing, advertising, schooling, organic and agricultural sciences, and commercial quality controls.

**Read or Download An Introduction to Categorical Data Analysis (Wiley Series in Probability and Statistics) PDF**

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

**Sample text**

It equals zero when π1 = π2 , that is, when the response is independent of the group classiﬁcation. Let p1 and p2 denote the sample proportions of successes. The sample difference p1 − p2 estimates π1 − π2 . For simplicity, we denote the sample sizes for the two groups (that is, the row totals n1+ and n2+ ) by n1 and n2 . 1) The standard error decreases, and hence the estimate of π1 − π2 improves, as the sample sizes increase. 1 For a signiﬁcance test of H0 : π1 = π2 , a z test statistic divides (p1 − p2 ) by a pooled SE that applies under H0 .

7. Report the P -value. d. 074). 05. 5 found binomial P -values for a clinical trial with y = 9 successes in 10 trials. Suppose instead y = 8. 2: a. 50. b. 50. c. 0 for the mid P -value? 15 If Y is a variate and c is a positive constant, then the standard deviation of the distribution of cY equals cσ (Y ). Suppose Y is a binomial variate, and let p = Y /n. a. √ Based on the binomial standard deviation for Y , show that σ (p) = [π(1 − π)/n]. b. 50. 16 Using calculus, it is easier to derive the maximum of the log of the likelihood function, L = log , than the likelihood function itself.

3 THE ODDS RATIO We will next study the odds ratio, another measure of association for 2 × 2 contingency tables. It occurs as a parameter in the most important type of model for categorical data. 25 = 3. 0 when a success is more likely than a failure. 0, a success is four times as likely as a failure. 0. We then expect to observe four successes for every one failure. When odds = 1/4, a failure is four times as likely as a success. We then expect to observe one success for every four failures. 8.

### An Introduction to Categorical Data Analysis (Wiley Series in Probability and Statistics) by Alan Agresti

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