All of the previous regression examples have used continuous dependent variables.
Logistic regression is used when the dependent variable is binary or dichotomous.
There are a number of differences in the assumption between traditional regression and logistic regression. The population means of the dependent variables at each level of the independent variable are not on a straight line, i.e., no linearity. The variance of the errors are not constant, i.e., no homogeneity of variance. The errors are not normally distributed, i.e., no normaility. The cases are independent.