Comparison Between Quadratic And Logistic Discrimination Function To Get On The Best Classification With Application

Abstract

The purpose of this research is to compare between quadratic and logistic discrimination function, also to identify the best method in discrimination which helps us to classify the data correctly. A several stages are starting from stratified sampling; classification errors and error rate if the pre-probability is equal and unequal by containing a few errors. Our choice of the correct path in analyzing the data helped us to identify the reasons that increase the errors in classification. In this study, we used some statistical measurements in terms of, the stratified sample, classification errors and the percentage of errors. The percentage of errors in the pre-probability is equal and unequal when the accumulation of errors in the square discrimination function was less than the accumulation of errors in the logistic discrimination function when the probability is equal, but when the probability is not equal, the results of the square function were much better than the square discrimination function. However, the accumulation of errors in the quadratic function is less than the accumulation of errors in the logistic function.