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dc.contributorVargas De Leon, Cruz
dc.contributorGodinez Jaimes, Flaviano
dc.contributor.advisorVARGAS DE LEON, CRUZ; 591497
dc.contributor.advisorGODINEZ JAIMES, FLAVIANO; 122953
dc.contributor.authorTino Salgado, Edilberta
dc.date.accessioned2023-03-24T18:44:57Z
dc.date.available2023-03-24T18:44:57Z
dc.date.issued2022-12
dc.identifier.urihttp://ri.uagro.mx/handle/uagro/3541
dc.description.abstractHierarchical data is a data type with a hierarchical or nested structure. One level ordinary regression models assume independence of the observations that make up a group, however this is almost always not the case. That is, it is expected that the observations of the same group are similar. Using an ordinary (one-level) regression model on hierarchical data can lead to abnormal statistical inferences caused by an increase in the type 1 error rate. The increase in the type 1 error rate arises from not considering the units of highest level and only consider the measurements of the lowest level where the response variable is measured (there is an underestimation of the standard errors that makes the parameters significant when in reality they are not).
dc.formatpdf
dc.language.isospa
dc.publisherUniversidad Autónoma de Guerrero (México)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.classificationCIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA::MATEMÁTICAS::ESTADÍSTICA
dc.titleModelación Bayesiana de datos multinivel en enfermedad periodontal.
dc.typeTesis de maestría
dc.type.conacytmasterThesis
dc.rights.accesopenAccess
dc.audiencegeneralPublic
dc.identificator1||12||1209
dc.format.digitalOriginBorn digital
dc.thesis.degreelevelMaestría
dc.thesis.degreenameMaestría en Matemáticas Aplicadas
dc.thesis.degreegrantorUniversidad Autónoma de Guerrero
dc.thesis.degreedepartmentFacultad de Matemáticas
dc.thesis.degreedisciplineCiencias Físico Matemáticas y Ciencias de la Tierra
dc.identifier.cvuagro18451


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