20. Generalized Linear Models
Not all data can be appropriately modeled with linear regression, because they are binomial (TRUE/FALSE), count data or some other form. To model these types of data, generalized linear models were developed. They are still modeled using a linear predictor, Xβ, but they are transformed using some link function. To the R user, fitting a generalized linear model requires barely any more effort than running a linear regression.
20.1 Logistic Regression
A very powerful and common model—especially in fields such as marketing and medicine—is logistic regression. The examples in this section will use the a subset of data from the 2010 American Community Survey (ACS) for New York State.1 ACS data contain a lot of information, ...
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