The Analysis of Binary Data
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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The Linear Logistic Model
The Empirical Logistic Transform
Exact Analysis for a Single Parameter
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2x2 contingency table 2x2 table Amer analysis applied associated asymptotic BEBKSON binary data binary response binomial Biometrics Biometrika Bradley-Terry model calculation Chapter chi-squared comparison computation conditional distribution confidence limits consider constant corresponding covariance matrix data of Table degrees of freedom denote discussion empirical logistic transform equations Example 1.1 explanatory variables factor finite population fitted given independent individual J. R. Statist linear logistic model linear model log likelihood logistic difference logistic function logistic regression logistic scale lung cancer mean and variance methods normally distributed nuisance parameters null hypothesis number of successes observed value obtained particular peptic ulcer plotted possible prob probability generating function probability of success procedure Proportion of successes random variables regressor variables residuals response curve sample serial order simple standard error stimulus level stimulus-response curve sufficient statistics sum of squares Suppose test statistic theory total number unknown parameters vector weighted least squares zero