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BROILERGROWTHPERFORMANCEANALYSISFROMCORRELATIONANALYSIS,MULTIPLELINEARREGRESSION,TONEURALNETWORKMEIYANXIAO,PEIJIEHUANG,PIYUANLIN,SHANGWEIYANCOLLEGEOFINFORMATICSSOUTHCHINAAGRICULTURALUNIVERSITYGUANGZHOU,CHINACORRESPONDINGAUTHORPJHUANGSCAUEDUCNABSTRACTTHEPURPOSEOFTHISSTUDYISTOINVESTIGATETHEDATAWEUSETHEBROILERGROWTHDATASETOFTHEMOSTFAMOUSFITTINGFORBROILERGROWTHPERFORMANCEPARAMETERSINTHISPAPER,POULTRYRAISINGCOMPANYINCHINATOEVALUATEOURAPPROACHANDTHEGRADUALADVANCINGANALYSISMETHODS,FROMCORRELATIONTHERESULTSSHOWTHEEFFECTIVENESSOFOURAPPROACHANALYSIS,MULTIPLELINEARREGRESSION,TONEURALNETWORK,AREPROPOSEDTHEMEANTECHNOLOGYROADMAPISFIRSTLY,CORRELATIONTHERESTOFTHISPAPERISORGANIZEDASFOLLOWSINTHENEXTANALYSISISUSEDTODETECTTHEDEGREEOFCORRELATIONBETWEENTHESECTION,WEPRESENTTHEGRADUALADVANCINGANALYSISMETHODSBROILERGROWTHPERFORMANCEPARAMETERANDTHECANDIDATEINPUTEXPERIMENTSAREPRESENTEDANDDISCUSSEDINSECTION3FINALLY,VARIABLESANDTHENCHOOSETHEPREDICTORVARIABLESTHATHAVEGOODSECTION4LISTSSOMECONCLUSIONSCORRELATIONWITHTHEDEPENDENTVARIABLETOBUILDTHEMULTIPLELINEARREGRESSIONORNEURALNETWORKPREDICTIONMODEL,ORBOTH,IIGRADUALADVANCINGANALYSISMETHODSACCORDINGTOTHELINEARDEGREEOFCORRELATIONSCOMBINEDPREDICTIONMAYBECHOSEONCEBOTHMODELSHAVEGOODPREDICTIONTHEEXPLOREMETHODSINOURSTUDYOFDATAFITTINGFORBROILERPERFORMANCESWEUSETHEBROILERGROWTHDATASETOFTHEMOSTGROWTHPERFORMANCEPARAMETERSISDEVELOPEDSTEPBYSTEP,FROMFAMOUSPOULTRYRAISINGCOMPANYINCHINATOEVALUATEOURCORRELATIONANALYSISTOMLR,ANDTHENTONONLINEARFITTINGAPPROACHANDTHERESULTSSHOWTHEEFFECTIVENESSOFOURAPPROACHMEANSBYNEURALNETWORKKEYWORDSGROWTHPERFORMANCE;CORRELATIONANALYSIS;MULTIPLEATECHNOLOGYROADMAPLINEARREGRESSION;NEURALNETWORK;BROILERBREEDINGTHETECHNOLOGYROADMAPOFOURGRADUALADVANCINGANALYSISMETHODSISSHOWNINFIG1IINTRODUCTIONBIOINFORMATICS1ISAPROMISINGYOUNGFIELDTHATAPPLIESCOMPUTERTECHNOLOGYINBIOLOGYANDDEVELOPSALGORITHMSANDMETHODSTOMANAGEANDANALYZEBIOLOGICALDATA2FORTHEMODERNPOULTRYBREEDINGCOMPANIES,ITISDESERVEDTOPREDICTTHEPOULTRYGROWTHPERFORMANCEPARAMETERS,SUCHASRATEFORSALE,FEEDINTAKE,DAILYGAINANDFEEDCONVERSIONRATIO,BASEDONTHEMASSIVEHISTORICALDATAGRADUALLYCUMULATEDINPRODUCTIONHOWEVER,BECAUSEOFTHECOMPLEXITYANDUNCERTAINTYBRINGBYTHEINFLUENCEOFENVIRONMENTALANDPHYSIOLOGICALFACTORS,INFORMATIONINTEGRATIONOFBIOLOGICALDATAISACHALLENGEINTHISPAPER,THEGRADUALADVANCINGANALYSISMETHODS,FROMCORRELATIONANALYSIS,MULTIPLELINEARREGRESSIONMLR3,TONEURALNETWORK4,AREPROPOSEDTOSTUDYTHEDATAFITTINGFORBROILERGROWTHPERFORMANCEPARAMETERSINBROILERBREEDING,SEASONALFACTORPLAYSANIMPORTANTPARTONTHEEFFECTOFSEASONALFACTORS,BROILERGROWTHPERFORMANCEFIGURE1TECHNOLOGYROADMAPOFTHEPROPOSEDMETHODSCANBEOBVIOUSLYDIFFERENTSOBROILERGROWTHPERFORMANCETHEASSOCIATIONBETWEENVARIABLESCANBELINEARORPARAMETERSHAVEOBVIOUSSEASONALVARIATIONSEASONALFACTORSNONLINEARCORRELATIONANALYSISISMOSTLYUSEDTOEVALUATELINEARINCLUDEAIRTEMPERATURE,PRECIPITATION,WINDSPEED,PRESSURE,RELATIONSHIPSASSOCIATIONSBETWEENTWOVARIABLESCANBERELATIVEHUMIDITY,ETCTHISPAPERTAKESTHEINFLUENCEOFTHEAIRANALYZEDWITHABIVARIATECORRELATIONANALYSISWHILETEMPERATURETOTHERATEFORSALEFOREXAMPLETOINTRODUCETHEASSOCIATIONSBETWEENONEDEPENDENTVARIABLEANDASETOFTWOBROILERGROWTHPERFORMANCEANALYSISMETHODSORMOREINDEPENDENTVARIABLES,WHICHHAVESTRONGTHISWORKISSUPPORTEDBYTHESCITECHRESEARCHPROJECTOFGUANGDONGPROVINCEUNDERGRANTNO2007A020300010,THENATIONAL863HIGHTECHRESEARCHDEVELOPMENTPLANOFCHINAUNDERGRANTNO2006AA10Z246,ANDTHENEWDISCIPLINESUPPORTINGFUNDOFSOUTHCHINAAGRICULTURALUNIVERSITYUNDERGRANTNO2007X022NOTLINEARENOUGHDEPENDENTVARIABLEINDEPENDENTVARIABLESCORRELATIONANALYSISCOMPARISONCOMBINEDPREDICTIONWHENBOTHHAVEGOODPREDICTRESPONSESMULTIPLELINEARREGRESSIONNEURALNETWORKSTRONGLINERCORRELATION9781424447138/10/25002010IEEECORRELATIONSWITHTHEDEPENDENTVARIABLE,CANBESTUDIEDUSINGMULTIPLECORRELATIONREGRESSIONANALYSIS,SUCHASMLRALTERNATIVELY,IFTHEDEGREEOFCORRELATIONSISNOTLINEARENOUGHBETWEENTHEDEPENDENTVARIABLEANDTHEINDEPENDENTVARIABLES,SOMENONLINEARFITTINGSPROVIDEGOODCHOOSEINTHENONLINEARFITTINGMETHODS,COMPARINGTOGOMPERTZTHATUSINGLEASTSQUARESINNONLINEARREGRESSION,NEURALNETWORKISPROVEDTOHASGOODABILITYTOPREDICTRESPONSES5FINALLY,INPRACTICALAPPLICATION,IFBOTHMLRANDNEURALNETWORKHAVEGOODPREDICTIONPERFORMANCES,WECANCONSIDERTHECOMBINEDPREDICTIONBCORRELATIONANALYSISACORRELATIONANALYSISISASTATISTICALPROCEDURETHATEVALUATESTHEASSOCIATIONBETWEENTHEDEPENDENTVARIABLEANDTHEINDEPENDENTVARIABLESRESPECTIVELYTHESIMPLESTWAYTOFINDOUTQUALITATIVELYTHECORRELATIONISTOPLOTTHEDATAANDWECANQUANTIFYTHEDEGREEOFCORRELATIONBYSPECIFYINGTHECORRELATIONCOEFFICIENTR,DEFINEDASYYINIXXIYXNRΣΣ−−−∑1111WHEREXANDXΣDENOTETHESAMPLEMEANANDTHESAMPLESTANDARDDEVIATIONRESPECTIVELYFORTHEVARIABLEXANDYANDYΣDENOTETHESAMPLEMEANANDTHESAMPLESTANDARDDEVIATIONRESPECTIVELYFORTHEVARIABLEYASSUMETHATAPERFECTLINEARRELATIONSHIPEXISTSBETWEENTHEVARIABLESXANDY,IE,BAXYIIFORI1,2,,NWITH0≠ANOWVERIFYUSINGTHEDEFINITIONSOFTHEMEANANDTHEVARIANCETHATBAXYANDXYAΣΣTHISIMPLIESFROM1THATRA/|A|ORINOTHERWORDS,R1IFA0ANDR1IFA0THECASER1CORRESPONDSTOTHEMAXIMUMPOSSIBLELINEARPOSITIVEASSOCIATIONBETWEENXANDY,MEANINGTHATALLTHEDATAPOINTSWILLLIEEXA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