Please use this identifier to cite or link to this item: http://monografias.ufrn.br/handle/123456789/5433
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dc.contributor.authorSilva Neto, Waldemar Alves da-
dc.date.accessioned2017-12-18T20:33:38Z-
dc.date.available2017-12-18T20:33:38Z-
dc.date.issued2017-12-07-
dc.identifier2013017241pr_BR
dc.identifier.citationNETO, Waldemar Alves da Silva. Aplicação de um modelo de regressão linear aos dados de balneabilidade das praias de Natal entre 2011 e 2015. 2017. 66 f. Monografia (Graduação) - Departamento de Estatística, Universidade Federal do Rio Grande do Norte, Natal, 2017.pr_BR
dc.identifier.urihttp://monografias.ufrn.br/jspui/handle/123456789/5433-
dc.description.abstractIn this work, we used weekly contamination data (number of \emph{Escherichia coli} bacteria) for the period from 2011 to 2015 to describe the water quality conditions of 15 points of the Natal coastline. Graphs, tables, and data summarization were used both at local level (each point) and at general level (all beaches together). Points NA-13 (Redinha/Potengi River) and NA-15 (Redinha Beach) obtained the worst contamination rates. The result of NA-13 should be related to the fact that the Potengi River is the main rainwater collector in the city. The point NA-15 is directly impacted by the sea currents that push the waters from the mouth of the Potengi River to this area, and the uncontrolled urban sprawl in the north zone of Natal, as well as the high concentration of bathers and trades on the beach. On the other hand, point NA-06 (Via Costeira) obtained the best results regarding the levels of contamination in the monitored period. This is probably due to the peculiar conditions of the region. It is an area of open sea, with low urban density (only hotels), few accesses to the beach and no commercial structure of bar and restaurant, being therefore an area of reduced anthropic impact. After the descriptive analysis, we adjusted multiple linear regression and simple linear regression models using as predictive variables, respectively, daily rainfall levels in the 6 days prior to each collection and the accumulated rainfall in those 6 days. The response variable was the number of bacteria counted in the weekly water samples by the most probable number method (MPN). The models were not satisfactory, nor did they fit the assumptions necessary for their acceptance and use for predicting the amount of bacteria in the water. We conclude that for the development of statistical tools for balneability prediction, it is necessary to use more explanatory variables, as well as more robust methods, which treat data inflated with zeros, since rainfall in the capital assumes zero values with high frequency due to the Tropical Climate of the city.pr_BR
dc.languagept_BRpr_BR
dc.publisherUniversidade Federal do Rio Grande do Nortepr_BR
dc.rightsopenAccesspr_BR
dc.subjectNatalpr_BR
dc.subjectEscherichia colipr_BR
dc.subjectAnálise de Regressãopr_BR
dc.subjectAnálise Descritivapr_BR
dc.titleAplicação de um modelo de regressão linear aos dados de balneabilidade das praias de Natal entre 2011 e 2015pr_BR
dc.typebachelorThesispr_BR
dc.contributor.referees1Nunes, Marcus Alexandre-
dc.contributor.referees2Pinho, André Luís de Santos-