In this chapter, we will consider regression models when the regressand is dichotomous or binary in nature. The data is of the form , where the dependent variable *Y*_{i}, *i* = 1, …, *n* are the observed binary output assumed to be independent (in the statistical sense) of each other, and the vector *X*_{i}, *i* = 1,…, *n*, are the covariates (independent variables in the sense of a regression problem) associated with *Y*_{i}.

In the previous chapter, we considered linear regression models where the regressand was assumed to be continuous along with the assumption of normality for the error distribution. Here, we will consider a Gaussian ...

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