By Alan Agresti

ISBN-10: 0470114754

ISBN-13: 9780470114759

ISBN-10: 0471226181

ISBN-13: 9780471226185

The 1st variation of this article has offered over 19,600 copies. besides the fact that, using statistical tools for express information has elevated dramatically in recent times, relatively for functions within the biomedical and social sciences. A moment variation of the introductory model of the e-book will go well with it well. Wiley additionally released a second variation of *Categorical info Analysis*, that's a sophisticated, extra technical textual content, in 2003.

Content:

Chapter 1 advent (pages 1–20):

Chapter 2 Contingency Tables (pages 21–64):

Chapter three Generalized Linear types (pages 65–98):

Chapter four Logistic Regression (pages 99–136):

Chapter five construction and making use of Logistic Regression versions (pages 137–172):

Chapter 6 Multicategory Logit versions (pages 173–203):

Chapter 7 Loglinear versions for Contingency Tables (pages 204–243):

Chapter eight types for Matched Pairs (pages 244–275):

Chapter nine Modeling Correlated, Clustered Responses (pages 276–296):

Chapter 10 Random results: Generalized Linear combined versions (pages 297–324):

Chapter eleven A ancient journey of express information research (pages 325–331):

**Read Online or Download An Introduction to Categorical Data Analysis, Second Edition PDF**

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**An Introduction to Categorical Data Analysis, Second Edition by Alan Agresti PDF**

The 1st variation of this article has bought over 19,600 copies. although, using statistical equipment for express info has elevated dramatically lately, fairly for purposes within the biomedical and social sciences. A moment version of the introductory model of the ebook will swimsuit it well.

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**Additional info for An Introduction to Categorical Data Analysis, Second Edition**

**Example text**

3) 1200 Total 1246 566 945 2757 Note: Estimated expected frequencies for hypothesis of independence in parentheses. Data from 2000 General Social Survey. would be rather unusual if the variables were truly independent. Both test statistics suggest that political party identification and gender are associated. 5 Residuals for Cells in a Contingency Table A test statistic and its P -value describe the evidence against the null hypothesis. A cell-by-cell comparison of observed and estimated expected frequencies helps us better understand the nature of the evidence.

We recommend inverting instead the binomial test using the mid P value. 05. This is available in some software, such as an R function (written by A. html. 6 Summary This chapter has introduced the key distributions for categorical data analysis: the binomial and the multinomial. It has also introduced maximum likelihood estimation and illustrated its use for proportion data using Wald, likelihood-ratio, and score methods of inference. The rest of the text uses ML inference for binomial and multinomial parameters in a wide variety of contexts.

We cannot estimate such proportions for this data set. For instance, about a third of the sample suffered MI. This is because the study matched each MI case with two controls, and it does not make sense to use 1/3 as an estimate of the probability of MI. We can estimate proportions in the reverse direction, for the conditional distribution of smoking status, given myocardial infarction status. 333 for women who had not suffered MI. When the sampling design is retrospective, we can construct conditional distributions for the explanatory variable, within levels of the fixed response.

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