Regression Models for Proportions
This chapter considers the problem of fitting binary response models to data in which there are multiple regressor variables that may be either discrete or continuous in nature. The linear logistic model, the most commonly used model for this type of response, provides estimates of parameters that are assumed to have linear effects on the log odds ratio, thus yielding values that can be interpreted as log odds ratios. The more flexible generalized linear models family that can readily be adapted for fitting many alternative forms for the relationship between exposure and disease outcome are considered: the log-linear hazard, the probit model, the linear odds model, and the linear power of the odds model. Exercises are provided at the end of the chapter.
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