Regression dilution in energy management patterns

Lewis Halsey, Andrea Perna

Research output: Contribution to journalArticlepeer-review

91 Downloads (Pure)


Analysis of some experimental biology data involves linear regression and interpretation of the resulting slope value. Usually the x-axis measurements include noise. Noise in the x-variable can create regression dilution, and many biologists are not aware of the implications – regression dilution results in an underestimation of the true slope value. This is particularly problematic when the slope value is diagnostic. For example, energy management strategies of animals can be determined from the regression slope estimate of mean energy expenditure against resting energy expenditure. Typically, energy expenditure is represented by a proxy such as heart rate, which adds substantive measurement error. With simulations and analysis of empirical data, we explore the possible effect of regression dilution on interpretations of energy management strategies. We conclude that unless r2 is very high, there is a good possibility that regression dilution will affect qualitative interpretation. We recommend some ways to contend with regression dilution, including the application of alternative available regression approaches under certain circumstances.

© 2019, The Company of Biologists Ltd. The attached document (embargoed until 27/03/2020) is an author produced version of a paper published in JOURNAL OF EXPERIMENTAL BIOLOGY uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it.
Original languageEnglish
Publication statusPublished - 27 Mar 2019

Cite this