dc.contributor | Seidel, Enio Júnior | |
dc.creator | Ribeiro, Tatiane Fontana | |
dc.date.accessioned | 2019-04-15T13:48:16Z | |
dc.date.available | 2019-04-15T13:48:16Z | |
dc.date.created | 2019-04-15T13:48:16Z | |
dc.date.issued | 2019-02-19 | |
dc.identifier | http://repositorio.ufsm.br/handle/1/16213 | |
dc.description.abstract | Linear 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.publisher | Universidade Federal de Santa Maria | |
dc.publisher | Brasil | |
dc.publisher | UFSM | |
dc.publisher | Centro de Ciências Naturais e Exatas | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.rights | Acesso Aberto | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.subject | Modelos lineares | |
dc.subject | Modelos lineares generalizados | |
dc.subject | Modelos aditivos generalizados para locação,escala e forma | |
dc.subject | Erva-mate | |
dc.subject | Rio Grande do Sul | |
dc.subject | Linear models | |
dc.subject | Generalized linear models | |
dc.subject | Generalized additive models for location, scale and shape | |
dc.subject | Yerbamate | |
dc.title | Modelos de regressão para o valor da produção de erva-mate no Rio Grande do Sul | |
dc.type | Trabalho de Conclusão de Curso de Especialização | |