Accounting for bias in regression coefficients with example from feed efficiency

Author(s)
Robinson, Dorothy L
Publication Date
2005
Abstract
Estimates of regression coefficients are biased if the independent (or x) variables contain errors (for example, measurement errors). Equations are derived for the amount of bias in bivariate regression where one independent variable contains significant error, but errors in the other are negligible. Results are tabulated for differing amounts of error and a range of correlations (from 0.1 to 0.9) between the two independent variables. The process of estimating residual feed intake (RFI) is used to illustrate biases present in real-life data. RFI is defined as the amount of feed eaten by an animal less what would be expected from the animal's metabolic weight and weight gain. Measurement errors of metabolic weight, especially if calculated from the mean of several weighings, are relatively small. In contrast, errors in weight gain may be substantial. Regression coefficients from fitting the RFI equation using two different estimates of weight gain are compared with equations derived from genotypic regression and feed standards tables. The unadjusted coefficients differ substantially, but are shown to be much more consistent after adjusting for bias using equations derived in this paper.
Citation
Livestock Production Science, 95(1-2), p. 155-161
ISSN
1872-6070
0301-6226
1871-1413
Link
Publisher
Elsevier BV
Title
Accounting for bias in regression coefficients with example from feed efficiency
Type of document
Journal Article
Entity Type
Publication

Files:

NameSizeformatDescriptionLink