In linear regression, the dependent variable y (response variable) is continuous and its estimated value can be thought of as a conditional mean estimation for each value of x. In this case, it is assumed that the variable y is distributed according to normal distribution. When the dependent variable is dichotomous, and can be coded as having two values, zero or one (such as on = one, off = zero), the theoretical distribution of reference should not be normal but binomial distribution.
In fact, as we have seen in Chapter 2, Basic Concepts – Simple Linear Regression, the linear model is based on the following regression equation:
Here, the values of the dependent variable can go from -∞ to +∞. All this does not agree with the expected values for a dichotomous variable, which as we have said, assumes only...
