Panjwani, Raja (2024) A Note on the Semantics of Linear Regression. [Preprint]

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Abstract

The use of linear regression is ubiquitous across the social and behavioral sciences, and yet researchers rarely hold that the variables in their target systems are in fact linearly related. This raises the question of how to interpret linear regression coefficients when there is ‘functional misspecification’ and the target system exhibits nonlinearity. Here, recent methodological discussions among practitioners have mirrored philosophical debates over scientific realism. In this paper, I frame the prevailing views in terms of their stance on the scientific realism debate. I then present a novel realist interpretation of linear regression - the ‘secant interpretation’ - based on a property I derive about the manner in which linear regression coefficients represent properties of
non-linear systems of interest.

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Item Type:	Preprint
Creators:	Creators Email ORCID Panjwani, Raja rp1223@georgetown.edu
Keywords:	data models; linear regression; scientific realism; semantics
Subjects:	General Issues > Data General Issues > Causation General Issues > Models and Idealization General Issues > Realism/Anti-realism
Depositing User:	Raja Panjwani
Date Deposited:	27 Dec 2024 14:03
Last Modified:	27 Dec 2024 14:03
Item ID:	24447
Subjects:	General Issues > Data General Issues > Causation General Issues > Models and Idealization General Issues > Realism/Anti-realism
Date:	26 December 2024
URI:	https://philsci-archive.pitt.edu/id/eprint/24447

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