Subsampling data was used to study how different data points affect logistic regression model parameters. New estimators, like regularized lasso and ridge, were suggested for better parameter estimation compared to traditional methods. These new estimators closely match maximum likelihood estimators and have simpler mathematical expressions. The approach was tested on real and simulated data, showing promising results for improving parameter estimation in logistic regression models.