Analysis of Binary Data, Second Edition
The first edition of this book (1970) set out a systematic basis for the analysis of binary data and in particular for the study of how the probability of 'success' depends on explanatory variables. The first edition has been widely used and the general level and style have been preserved in the second edition, which contains a substantial amount of new material. This amplifies matters dealt with only cryptically in the first edition and includes many more recent developments. In addition the whole material has been reorganized, in particular to put more emphasis on m.aximum likelihood methods.
There are nearly 60 further results and exercises. The main points are illustrated by practical examples, many of them not in the first edition, and some general essential background material is set out in new Appendices.
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Special logistic analyses
Some related approaches
More complex responses
5 Techniques for inference
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analysis apply approach approximately assumed assumptions asymptotic binary data binary response binary variables binomial calculate chi-squared components conditional distribution consider constant contingency table continuous corresponding covariance matrix defined degrees of freedom dependence discussion empirical Bayes empirical logistic transform equation example explanatory variables exponential family factor fitted formulation given groups independent interaction interpretation involved ith individual large number likelihood function likelihood ratio linear logistic model linear model linear regression logistic difference logistic regression logistic scale main effects maximum likelihood estimate mean and variance measurement methods normally distributed Note nuisance parameters null hypothesis number of successes obtained overdispersion pair particular plotted possible preference probability of success problem procedure profile log likelihood random variables relation relevant residuals response variable sample Section 2.1 significance simple small number specific standard error stimulus sufficient statistics Suppose test statistic total number treatment effect trials unknown parameters vector weighted least squares zero