By Alan Agresti
Praise for the 1st Edition
"This is a wonderful textual content from which to educate express facts research, at numerous degrees. . . [t]his publication will be very hugely recommended."
—Short ebook Reviews
"Of nice curiosity to strength readers is the range of fields which are represented within the examples: future health care, monetary, executive, product advertising and marketing, and activities, to call a few."
—Journal of caliber Technology
"Alan Agresti has written one other fantastic account of the research of express data."
The use of statistical tools for express info is ever expanding in today's global. An creation to express information research, moment Edition presents an utilized advent to an important tools for reading specific info. This new version summarizes equipment that experience lengthy performed a well known position in information research, corresponding to chi-squared checks, and likewise locations exact emphasis on logistic regression and different modeling recommendations for univariate and correlated multivariate specific responses.
This moment version features:* new chapters at the equipment for clustered information, with an emphasis on generalized estimating equations (GEE) and random results types* A unified standpoint according to generalized linear types* An emphasis on logistic regression modeling* An appendix that demonstrates using SAS(r) for all tools* An wonderful ancient point of view at the improvement of the equipment* really expert equipment for ordinal facts, small samples, multicategory information, and coupled pairs* greater than a hundred analyses of genuine facts units and approximately three hundred exercises
Written in an utilized, nontechnical kind, the booklet illustrates tools utilizing a wide selection of genuine information, together with clinical medical trials, drug use via little ones, basketball taking pictures, horseshoe crab mating, environmental critiques, correlates of happiness, and lots more and plenty more.
An creation to specific facts research, moment Edition is a useful device for social, behavioral, and biomedical scientists, in addition to researchers in public well-being, advertising and marketing, schooling, organic and agricultural sciences, and commercial qc.
Read or Download An Introduction to Categorical Data Analysis (Wiley Series in Probability and Statistics) PDF
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Extra info for An Introduction to Categorical Data Analysis (Wiley Series in Probability and Statistics)
Note that the test with mid P -value can be “conservative” [having actual P (type I error) below the desired value] or “liberal” [having actual P (type I error) above the desired value]. The test with the ordinary P -value cannot be liberal. 96) is a solution to the equation (1 + √ z02 /n)π02 + (−2p − z02 /n)π0 + p 2 = 0. 4 for the probability of success when a clinical trial has nine successes in 10 trials. 1 cross classiﬁes a sample of Americans according to their gender and their opinion about an afterlife.
We should not trust it for such a small n as in this example (n = 10). Likelihood-ratio inference and score-test based inference are better in terms of actual error probabilities coming close to matching nominal levels. A marked divergence in the values of the three statistics indicates that the distribution of the ML estimator may be far from normality. In that case, small-sample methods are more appropriate than large-sample methods. 3 Small-Sample Binomial Inference For inference about a proportion, the large-sample two-sided z score test and the conﬁdence interval based on that test (using the null hypothesis standard error) perform reasonably well when nπ ≥ 5 and n(1 − π) ≥ 5.
A table of this form that displays counts of outcomes in the cells is called a contingency table. A table that cross classiﬁes two variables is called a two-way contingency table; one that cross classiﬁes three variables is called a three-way contingency table, and so forth. A two-way table with I rows and J columns is called an I × J (read I –by–J ) table. 1 is a 2 × 2 table. 1 Joint, Marginal, and Conditional Probabilities Probabilities for contingency tables can be of three types – joint, marginal, or conditional.