Parameter estimation in logistic regression using the maximum likelihood approach and the discriminant function approach is revisited. A bootstrap estimation of the standard errors for the estimates of the model parameters in discriminant function approach is obtained. The asymptotic distributions are compared in both the estimation procedures using bootstrap sampling. All the computations are performed for three different vastly used data sets and analyzed.