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Categorical Data Analysis by Example by Upton PDF

By Upton

ISBN-10: 1119307937

ISBN-13: 9781119307938

This publication is geared toward all those that desire to detect tips to study express information with no getting immersed in complex arithmetic and without having to go through a large number of prose. it truly is aimed toward researchers with their very own information able to be analyzed and at scholars who would favor an approachable replacement view of the subject.
Each new subject in express info research is illustrated with an instance that readers can observe to their very own units of information. in lots of situations, R code is given and excerpts from the ensuing output are awarded. within the context of log-linear versions for cross-tabulations, specialties of the home were integrated: using cobweb diagrams to get visible info touching on major interactions, and a technique for detecting outlier type combos. The R code used for those is out there and will be freely tailored. moreover, this book:
• makes use of an instance to demonstrate each one new subject in specific data
• presents a transparent rationalization of a big subject
• Is comprehensible to such a lot readers with minimum statistical and mathematical backgrounds
• comprises examples which are followed by means of R code and ensuing output
• contains starred sections that offer extra heritage information for readers

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Additional info for Categorical Data Analysis by Example

Sample text

The data consist of the frequencies with which the I × J possible category combinations occur. In this chapter both I and J will be greater than 2. We considered 2 × 2 tables in Chapter 3 and will look at 2 × J tables in Chapter 7. 1 NOTATION Consider a table with I rows, J columns, and IJ cells. Denote the classifying variables as A and B, and denote the categories as A1 , A2 , … , AI and B1 , B2 , … , BJ . , there are fij items that simultaneously belong to categories Ai and Bj ). Using the suffix 0 to denote totals gives: ∑ ∑ ∑∑ fij = f0j , fij = fi0 , fij = f00 .

1967) On the bias of various estimators of the logit and its variance. Biometrika, 54, 181–187. Irwin, J. O. (1935) Tests of significance for differences between percentages based on small numbers. Metron, 12, 83–94. Rice, W. R. (1988) A new probability model for determining exact P-values for 2 × 2 contingency tables when comparing binomial proportions. Biometrics, 44, 1–22. Upton, G. J. G. (1982) A comparison of tests for the 2 × 2 comparative trial. J. R. Stat. Soc. A, 45, 86–105. Upton, G.

O. (1949) The combination of probabilities arising from data in discrete distributions. Biometrika, 36, 370–382. Pearson, K. (1900) On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos. Mag. Ser. 5, 50(302), 157– 175. Wilson, E. B. (1927) Probable inference, the law of succession, and statistical inference. J. Am. Stat. , 22, 209–212. ****************************************************************** ************************************************************************* ********************************************************* ****************************************************************** ************************************************************************* ********************************************************* CHAPTER 3 THE 2 × 2 CONTINGENCY TABLE The 2 × 2 contingency table consists of just four numbers arranged in two rows with two columns to each row; a very simple arrangement.

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