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 Yi, i = 1, …, n are the observed binary output assumed to be independent (in the statistical sense) of each other, and the vector Xi, i = 1,…, n, are the covariates (independent variables in the sense of a regression problem) associated with Yi.
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 (normal) model for the binary regression model, which is more widely known as the probit model. A more generic model has emerged during the past four decades in the form of logistic regression model. We will consider the logistic regression model for the rest of the chapter. The approach in this chapter will be on the following topics:
The binary...