dc.contributorSeidel, Enio Júnior
dc.creatorRibeiro, Tatiane Fontana
dc.date.accessioned2019-04-15T13:48:16Z
dc.date.available2019-04-15T13:48:16Z
dc.date.created2019-04-15T13:48:16Z
dc.date.issued2019-02-19
dc.identifierhttp://repositorio.ufsm.br/handle/1/16213
dc.description.abstractLinear regressionmodelsthatsupposenormalresponsevariableswerewidelyuseduntil more flexibletechniqueswereintroduced.Realsituationsareverycomplex,thusitisdifficult that variablescomplywiththeassumptionsoftheclassiclinearmodel,likethenormalityof the responsevariable.Alternativetechniqueswerethenproposed,like:thegeneralizedlinear models (GLM)andgeneralizedadditivemodelsforlocation,scaleandshape(GAMLSS).GLM main advantageinrelationtolinearmodel(LM)technique,becausetheresponsevariablecan followanydistributionoftheexponentialfamily,besidesofthenormaldistribution.Itisalso possible tomodelotherparametersofthedistributionbasedonthecovariables.Byconsidering the relevanceofthepermanentcultureofyerbamatetotheeconomyRS,thequantityofpublic statistics averableaboutthesubjectandthetechniquesofmodellingstatisticmentioned,theof purpose ofthisstudyistoobtainaregressionmodeltoexplainthevariationoftheproduction valueoftheyerbamateintheRS.ThedatasetwasobtainedfromFoundationofEconomyand Statistics. Thisdatasethastheresponsevariable:productionvalueofyerbamateandquan- titativecovariablesassociatedtoitsproductionandcommercializationin2016.Itisaddedto data setqualitativecovariablesreferringtopolesandmicroregionsstudiedlikedummyvari- ables. Thereafter,descriptivestatisticanalysisisdoneinordertoidentifythebehaviorofall variables.Regressionmodelsareobtainedfromclassicaltechnique(LM)tothemoresophisti- cated (GAMLSS).ItisnotedthattheGAMLSSmodelhadthebestfitaccordingwithmodels selection criteriaandgraphicalanalysisoftheresiduals.FinalGAMLSSmodelatthe5%sig- nificance levelthesignificantquantitativecovariableswere:quantityproduced,harvestedarea and areaforharvesting.Thiscovariablespresentpositivecontributiontoresponsevariableac- cording linearrelationshowedthroughthecorrelationanalysis.PoloPlanaltoMissõeswas the onlysignificantanditpresentedpositiveeffecttoproductionvalue,becauseitissecond largeststateproducer.Thesignificantmicrorregionswere:CaxiasdoSul,Erechim,Frederico Westphalen,Gramado-Canela,Guaporé,Lajeado-Estrela,SantaCruzdoSul,SantaRosaeTrês Passos.ExceptingGramado-Canelamicrorregion,theothersmicrorregionspresentednegative effecttodependentvariable.
dc.publisherUniversidade Federal de Santa Maria
dc.publisherBrasil
dc.publisherUFSM
dc.publisherCentro de Ciências Naturais e Exatas
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsAcesso Aberto
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.subjectModelos lineares
dc.subjectModelos lineares generalizados
dc.subjectModelos aditivos generalizados para locação,escala e forma
dc.subjectErva-mate
dc.subjectRio Grande do Sul
dc.subjectLinear models
dc.subjectGeneralized linear models
dc.subjectGeneralized additive models for location, scale and shape
dc.subjectYerbamate
dc.titleModelos de regressão para o valor da produção de erva-mate no Rio Grande do Sul
dc.typeTrabalho de Conclusão de Curso de Especialização


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