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

By Alan Agresti

The 1st version of this article has bought over 19,600 copies. besides the fact that, using statistical tools for specific information has elevated dramatically in recent times, fairly for functions within the biomedical and social sciences. A moment variation of the introductory model of the booklet will swimsuit it well. Wiley additionally released a second variation of Categorical information Analysis, that is a complicated, extra technical textual content, in 2003.

Chapter 1 creation (pages 1–20):
Chapter 2 Contingency Tables (pages 21–64):
Chapter three Generalized Linear versions (pages 65–98):
Chapter four Logistic Regression (pages 99–136):
Chapter five construction and employing Logistic Regression types (pages 137–172):
Chapter 6 Multicategory Logit types (pages 173–203):
Chapter 7 Loglinear versions for Contingency Tables (pages 204–243):
Chapter eight versions for Matched Pairs (pages 244–275):
Chapter nine Modeling Correlated, Clustered Responses (pages 276–296):
Chapter 10 Random results: Generalized Linear combined types (pages 297–324):
Chapter eleven A historic journey of express facts research (pages 325–331):

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Extra info for An Introduction to Categorical Data Analysis, Second Edition

Sample text

The ratio of failure probabilities, (1 − π1 )/(1 − π2 ), takes a different value than the ratio of the success probabilities. When one of the two outcomes has small probability, normally one computes the ratio of the probabilities for that outcome. 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.

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.

12 To collect data in an introductory statistics course, recently I gave the students a questionnaire. One question asked whether the student was a vegetarian. ” They were not a random sample, but let us use these data to illustrate inference for a proportion. 50. “c01” — 2007/1/29 — page 18 — #18 19 PROBLEMS a. What happens √ when you try to conduct the “Wald test,” for which z = (p − π0 )/ [p(1 − p)/n] uses the estimated standard error? b. 3) for π. Is it believable? ) √ c. Conduct the “score test,” for which z = (p − π0 )/ [π0 (1 − π0 )/n] uses the null standard error.

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