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- Carcass composition (1)
- Empirical Bayes (1)
- Kansas Agricultural Experiment Station contribution; no. 14-306-S; Report of progress (Kansas State University. Agricultural Experiment Station and Cooperative Extension Service); 1105; Beef cattle; Forage crops; Soil management; Water management; Cropping systems (1)
- Proportional measurement error (1)
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Articles 1 - 2 of 2
Full-Text Articles in Agriculture
Developing Prediction Equations For Carcass Lean Mass In The Prescence Of Proportional Measurement Error, Zachary J. Hass, Ziqi Zhou, Bruce A. Craig
Developing Prediction Equations For Carcass Lean Mass In The Prescence Of Proportional Measurement Error, Zachary J. Hass, Ziqi Zhou, Bruce A. Craig
Conference on Applied Statistics in Agriculture
Published prediction equations for carcass lean mass are widely used by commercial pork producers for carcass valuation. These regression equations have been derived under the assumption that the predictors, such as back fat depth, are measured without error. In practice, however, it is known that these measurements are imperfect, with a variance that is proportional to the mean. In this paper, we consider both a linear and quadratic true relationship and compare regression fits among two methods that account for this error versus simply ignoring the additional error. We show that biased estimates of the relationship result if measurement error …
2014 Agricultural Research Southeast Agricultural Research Center, Kansas State University. Agricultural Experiment Station And Cooperative Extension Service
2014 Agricultural Research Southeast Agricultural Research Center, Kansas State University. Agricultural Experiment Station And Cooperative Extension Service
Kansas Agricultural Experiment Station Research Reports
No abstract provided.