1、Designation:F234005(Reapproved 2010)Standard Specification forDeveloping and Validating Prediction Equation(s)orModel(s)Used in Connection with Livestock,Meat,andPoultry Evaluation Device(s)or System(s)to DetermineValue1This standard is issued under the fixed designation F2340;the number immediately
2、 following the designation indicates the year oforiginal adoption or,in the case of revision,the year of last revision.A number in parentheses indicates the year of last reapproval.Asuperscript epsilon()indicates an editorial change since the last revision or reapproval.1.Scope1.1 This specification
3、 covers methods to collect and analyzedata,document the results,and make predictions by anyobjective method for any characteristic used to determine valuein any species using livestock,meat,and poultry evaluationdevices or systems.1.2 This standard does not purport to address all of thesafety concer
4、ns,if any,associated with its use.It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determine the applica-bility of regulatory requirements prior to use.2.Referenced Documents2.1 ASTM Standards:2F2463 Terminology for Livestock,Meat,and Pou
5、ltry Evalu-ation Systems3.Terminology3.1 For definitions of terms used in this specification,referto Terminology F2463.3.2 Definitions of Terms Specific to This Standard:3.2.1 accuracy,nstatement of the exactness with which ameasurement approaches the true measure for that character-istic;accuracy i
6、s contrasted with precision,which is concernedwith the repeatability of the measurements.Therefore,with alarge bias,a measurement may be of high precision,but of lowaccuracy.3.2.2 calibration data set,ndata set used to develop theinitial prediction equations;same as developmental or predic-tion data
7、 set.3.2.3 coeffcient of determination,npercentage of variabil-ity in the response(dependent)variable that can be explainedby the prediction equation.R25 1 2(y 2 y!2(y 2 y!23.2.4 root mean square error for calibration,nsquare rootof the sum of squared residuals divided by nc(k+1),wherencis the sampl
8、e size for the calibration data set,and k is thenumber of explanatory variables in the prediction equation.(y 2 y!2nc2k11!3.2.5 root mean square error for validation,nsquare rootof the sum of squared residuals divided by ny,where nyis thesample size for the validation data set.(y 2 y!2nv3.2.6 valida
9、tion data set,nthe data set used to test thepredictive accuracy of the equations developed from thecalibration data set.3.2.7 value,commerce,nmeasure of economic worth incommerce.4.Significance and Use4.1 The procedures in this specification are to be used by allparties interested in predicting comp
10、osition or quality,or both,for the purpose of establishing value based upon device orsystem measurements.Whenever new prediction equations areestablished,or when a change is experienced that could affectthe performance of existing equations,these procedures shallbe used.5.Procedure5.1 Experimental D
11、esign:5.1.1 Define the Population for Development of a PredictionEquation:5.1.1.1 To establish the predictive ability and validity of anequation(s)using measures(independent variables)from an1This specification is under the jurisdiction of ASTM Committee F10 onLivestock,Meat,and Poultry Evaluation S
12、ystems and is the direct responsibility ofSubcommittee F10.40 on Predictive Accuracy.Current edition approved Sept.1,2010.Published December 2010.Originallyapproved in 2004.Last previous edition approved in 2005 as F2340 05.DOI:10.1520/F2340-05R10.2For referenced ASTM standards,visit the ASTM websit
13、e,www.astm.org,orcontact ASTM Customer Service at serviceastm.org.For Annual Book of ASTMStandards volume information,refer to the standards Document Summary page onthe ASTM website.Copyright ASTM International,100 Barr Harbor Drive,PO Box C700,West Conshohocken,PA 19428-2959.United States1 evaluati
14、on device or system,it is necessary to define thepopulation on which the prediction model is intended to beused.(1)The species on which measurements will be made mustbe defined.(2)The population for scope of use must be clearly defined.This may include,but is not limited to,factors such asgeographic
15、al location,gender,age,breed type,or any otherfactor that may affect the equation accuracy.(3)The characteristic to be predicted must be clearlydefined.5.1.2 Select a Sample Population for Development of aPrediction Equation:5.1.2.1 The sample size for the calibration data set must beat a minimum 10
16、k,where k is the number of variables in theprediction equation,or 100 observations,whichever is greater.The sample size for the validation data set must be at least20%of the size of the calibration validation data set.Forexample,if the prediction equation has five explanatoryvariables,the calibration data set will require a minimum of100 observations and the validation set must have at least 20observations.These are minimal requirements;larger samplesizes are encouraged,keeping in mind that the
