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TESTING THE ASSUMPTIONS OF A LINEAR REGRESSION MODEL. CASE STUDY: EUROP PIG CARCASS CLASSIFICATION SYSTEM IN ROMANIA
Abstract
In Romania, pig carcass classification started in March 2006 and, according to the EU regulation, it was implemented as a compulsory activity in all slaughterhouses, regardless their size. At present, the lean meat content is predicted on the basis of objective carcass measurements obtained with the “Two Points” method applied with the ruler (Zwei Punkte method) or with two optical probes (Fat-O-Meat'er and OptiGrade-Pro). In order to meet the European Commission's requirements regarding the harmonization of the carcass classification systems in European Union, the two optical probes were calibrated on the same dissection trial carried out in 2007. The prediction formula for the two optical probes was calculated using the method of linear regression. However, the existing working documents do not report any tests having been done on the five principal assumptions which justify the use of linear regression models for purposes of prediction: i/ lack of multicollinearity in the predictors, ii/ linearity of the relationship between dependent and independent variables; iii/ independence of the errors; iv/ homoscedasticity of the errors and v/ normality of the error distribution. If any of these assumptions is violated, then the economic insights yielded by a regression model may be inefficient. The purpose of this paper is to identify weather the existing regression model meets the above mentioned assumptions and, in case it doesn't, to develop an appropriate regression model.
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