On parameter estimation in the physics lab based on inverting a slope regression coefficient

TL;DR

This paper discusses how to accurately estimate parameters in physics experiments using linear regression, emphasizing clear guidelines for selecting predictor and predicted variables to reduce measurement uncertainty and inconsistencies.

Contribution

It provides a clear framework for choosing predictor and response variables in linear regression within physics labs, improving measurement accuracy and consistency.

Findings

Guidelines for variable selection in regression analysis

Reduction of measurement uncertainty in physics experiments

Clarification of statistical application in undergraduate labs

Abstract

Measurement uncertainty is a non trivial aspect of the laboratory component of most undergraduate physics courses. Confusion about the application of statistical tools calls for the elaboration of guidelines and the elimination of inconsistencies were possible. Linear regression is one of the fundamental statistical tool often used in a first year physics laboratory setting. In what follows we present an argument that leads to an unambiguous choice of (a) variable(s) to be used as predictor(s) and variable to be predicted.