BIG DATA AND THE CENTRAL LIMIT THEOREM: A STATISTICAL LEGEND

BIG DATA Y EL TEOREMA DEL LÍMITE CENTRAL: UNA LEYENDA ESTADÍSTICA

dc.creatorAllende-Alonso, S.
dc.creatorBouza-Herrera, C. N
dc.creatorRizvi, S. E. H
dc.creatorSautto-Vallejo, J. M
dc.date2023-04-11
dc.date.accessioned2023-05-22T20:48:57Z
dc.date.available2023-05-22T20:48:57Z
dc.identifierhttps://revistas.uh.cu/invoperacional/article/view/2694
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6330267
dc.descriptionNowadays we deal with Big-Data commonly. The users of statistics rely on having a large sample size n for using the statistical methods based on normality. Usual inference methods are typically based on considering the Normal as the limit distributions of the sample mean for a large n. With large enough sample sizes (> 30 or 40), the violation of the normality assumption should not cause major problems. This fact implies that we can use parametric procedures even when the data are not normally distributed. Al least a goodness-of-fit test must be performed for accepting whether normality is valid or not. Monte Carlo (MC) techniques are used for selecting independent random samples of populations of means of three variables of importance in web network management. Different tests are performed to establish the acceptance of the normality. We did not find reliable results even for samples of size 10 000en-US
dc.descriptionNowadays we deal with Big-Data commonly. The users of statistics rely on having a large sample size n for using the statistical methods based on normality. Usual inference methods are typically based on considering the Normal as the limit distributions of the sample mean for a large n. With large enough sample sizes (> 30 or 40), the violation of the normality assumption should not cause major problems. This fact implies that we can use parametric procedures even when the data are not normally distributed. Al least a goodness-of-fit test must be performed for accepting whether normality is valid or not. Monte Carlo (MC) techniques are used for selecting independent random samples of populations of means of three variables of importance in web network management. Different tests are performed to establish the acceptance of the normality. We did not find reliable results even for samples of size 10 000es-ES
dc.formatapplication/pdf
dc.languageeng
dc.publisherDepartamento de Matemática Aplicada. Facultad de Matemática y Computación. Universidad de La Habanaen-US
dc.relationhttps://revistas.uh.cu/invoperacional/article/view/2694/2349
dc.rightshttps://creativecommons.org/licenses/by/4.0es-ES
dc.sourceInvestigación Operacional; Vol. 40 No. 1 (2019): SPECIAL ISSUE: CONTRIBUTIONS IN MATHEMATICAL MODELING WITH IMPACT IN MEDICAL AND ENVIRONMENTS /NÚMERO ESPECIAL : CONTRIBUCIONES EN MODELACIÓN MATEMÁTICA CON IMPACTO EN MEDICINA Y MEDIO AMBIENTEen-US
dc.sourceInvestigación Operacional; Vol. 40 Núm. 1 (2019): SPECIAL ISSUE: CONTRIBUTIONS IN MATHEMATICAL MODELING WITH IMPACT IN MEDICAL AND ENVIRONMENTS /NÚMERO ESPECIAL : CONTRIBUCIONES EN MODELACIÓN MATEMÁTICA CON IMPACTO EN MEDICINA Y MEDIO AMBIENTEes-ES
dc.source2224-5405
dc.subjectgrandes masas de datoses-ES
dc.subjectpruebas de normalidades-ES
dc.subjectnormalidad asintótica de mediases-ES
dc.subjectBig-Dataen-US
dc.subjectnormality testsen-US
dc.subjectasymptotic normality of meansen-US
dc.titleBIG DATA AND THE CENTRAL LIMIT THEOREM: A STATISTICAL LEGENDen-US
dc.titleBIG DATA Y EL TEOREMA DEL LÍMITE CENTRAL: UNA LEYENDA ESTADÍSTICAes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeArticlesen-US
dc.typeArtículoes-ES


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