Alan Agresti(auth.)'s Analysis of Ordinal Categorical Data, Second Edition PDF

By Alan Agresti(auth.)

ISBN-10: 0470082895

ISBN-13: 9780470082898

ISBN-10: 0470594004

ISBN-13: 9780470594001

Statistical science’s first coordinated handbook of tools for reading ordered specific information, now totally revised and up to date, keeps to provide functions and case reports in fields as various as sociology, public health and wellbeing, ecology, advertising, and pharmacy. Analysis of Ordinal specific info, moment Edition offers an creation to easy descriptive and inferential tools for specific facts, giving thorough insurance of latest advancements and up to date equipment. specified emphasis is put on interpretation and alertness of equipment together with an built-in comparability of the on hand innovations for studying ordinal information. Practitioners of statistics in govt, (particularly pharmaceutical), and academia will wish this new edition.Content:
Chapter 1 creation (pages 1–8):
Chapter 2 Ordinal possibilities, ratings, and Odds Ratios (pages 9–43):
Chapter three Logistic Regression types utilizing Cumulative Logits (pages 44–87):
Chapter four different Ordinal Logistic Regression types (pages 88–117):
Chapter five different Ordinal Multinomial reaction types (pages 118–144):
Chapter 6 Modeling Ordinal organization constitution (pages 145–183):
Chapter 7 Non?Model?Based research of Ordinal organization (pages 184–224):
Chapter eight Matched?Pairs info with Ordered different types (pages 225–261):
Chapter nine Clustered Ordinal Responses: Marginal versions (pages 262–280):
Chapter 10 Clustered Ordinal Responses: Random results versions (pages 281–314):
Chapter eleven Bayesian Inference for Ordinal reaction facts (pages 315–344):

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Additional resources for Analysis of Ordinal Categorical Data, Second Edition

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5 6 For example, in SAS, using the LRCI option in PROC GENMOD. html. 5 ORDINAL PROBABILITIES, SCORES, AND ODDS RATIOS Confidence Intervals for Measures Using P(Y\ > Yz) We now consider the stochastic superiority measure a = P(Y\ >Yi) + \ P(Y\ = Y2) for comparing two groups on an ordinal response. A confidence interval for a implies a corresponding confidence interval for Δ = P{Y\ > Y2) — P(Y2>Yi), since Δ = 2a - 1. For independent multinomial samples of sizes n\ and ni from the two rows, Halperin et al.

3. Although a,·* from applying a to compare two groups i and k is not determined by the a values comparing group /' to the marginal distribution of Y and comparing group k to the marginal distribution, models can be specified in which this type of simplicity occurs. 4)] is logit (aik) = r; - rk, with a constraint such as r r = 0. Semenya et al. (1983) proposed a weighted least squares analysis for this model. Kawaguchi and Koch (2010) generalized this model in the context of crossover studies. 4.

In this scenario we need K > 5 strata before the middle one has an odds ratio within 10% of the limiting value. 12. 66 variable, when the marginal XY association is very strong. This tendency is not as severe when the marginal XY association is weaker. 50. 13). Cochran (1968) showed similar results for cases in which Y is quantitative and X is binary, in the context of reducing bias in comparing two groups in an observational study. When a quantitative variable can be measured in an essentially continuous manner, we are usually better off doing so rather than collapsing the variable into a few ordered categories.

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Analysis of Ordinal Categorical Data, Second Edition by Alan Agresti(auth.)


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