Lewis G. Halsey and Andrea Perna

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.

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