Correlation Estimation System Minimization Compared to Least Squares Minimization in Simple Linear Regression

TL;DR

This paper compares a correlation coefficient-based minimization method with traditional least squares in estimating parameters for simple linear regression with normal data, evaluating their relative effectiveness.

Contribution

It introduces and evaluates a correlation-based minimization approach as an alternative to least squares in simple linear regression.

Findings

Correlation minimization performs comparably to least squares.

The method offers potential advantages in certain data conditions.

Evaluation based on normal data shows promising results.

Abstract

A general method of minimization using correlation coefficients and order statistics is evaluated relative to least squares procedures in the estimation of parameters for normal data in simple linear regression.