Implicit Regression: Detecting Constants and Inverse Relationships with Bivariate Random Error

TL;DR

This paper introduces implicit regression methods to identify constants and inverse relationships among variables with random errors, expanding traditional regression analysis.

Contribution

It presents new techniques within implicit regression to detect constants and inverse relationships, addressing errors in both variables.

Findings

Methods successfully identify constants in variables.

Techniques detect inverse relationships with random errors.

Implications for improved data analysis in complex systems.

Abstract

In 2011, Wooten introduced Non-Response Analysis the founding theory in Implicit Regression where Implicit Regression treats the variables implicitly as codependent variables and not as an explicit function with dependent or independent variables as in standard regression. The motivation of this paper is to introduce methods of implicit regression to determine the constant nature of a variable or the interactive term, and address inverse relationship among measured variables with random error present in both directions.