dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Federal de Uberlândia (UFU)
dc.contributorInstituto de Tecnologia
dc.contributorUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T18:57:23Z
dc.date.accessioned2022-12-20T00:51:29Z
dc.date.available2022-04-28T18:57:23Z
dc.date.available2022-12-20T00:51:29Z
dc.date.created2022-04-28T18:57:23Z
dc.date.issued2009-07-01
dc.identifierBioscience Journal, v. 25, n. 4, p. 90-100, 2009.
dc.identifier1516-3725
dc.identifier1981-3163
dc.identifierhttp://hdl.handle.net/11449/219775
dc.identifier2-s2.0-84858173608
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5399904
dc.description.abstractThe objective of this work was to carry a descriptive analysis in the monthly precipitation of rainfall stations from Rio de Janeiro State, Brazil, using data of position and dispersion and graphical analyses, and to verify the presence of seasonality and trend in these data, with a study about the application of models of time series. The descriptive statistics was to characterize the general behavior of the series in three stations selected which present consistent historical series. The methodology of analysis of variance in randomized blocks and the determination of models of multiple linear regression, considering years and months as predictors variables, disclosed the presence of seasonality, what allowed to infer on the occurrence of repetitive natural phenomena throughout the time and absence of trend in the data. It was applied the methodology of multiple linear regression to removal the seasonality of these time series. The original data had been deducted from the estimates made by the adjusted model and the analysis of variance in randomized blocks for the residues of regression was preceded again. With the results obtained it was possible to conclude that the monthly rainfall present seasonality and they don't present trend, the analysis of multiple regression was efficient in the removal of the seasonality, and the rainfall can be studied by means of time series.
dc.languageeng
dc.languagepor
dc.relationBioscience Journal
dc.sourceScopus
dc.subjectClimate
dc.subjectMultiple regression
dc.subjectRainfall statistics
dc.subjectTime series
dc.titlePrecipitação pluviométrica mensal no Estado do Rio de Janeiro: Sazonalidade e tendência
dc.typeArtículos de revistas


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