Detalhes bibliográficos
| Ano de defesa: |
2016 |
| Autor(a) principal: |
GUERRA, Glauce da Silva
 |
| Orientador(a): |
MARTINELLI, Luiz Antonio |
| Banca de defesa: |
SIKOV, Anna,
LIMA, Claudia Regina Oliveira de Paiva |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal Rural de Pernambuco
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Biometria e Estatística Aplicada
|
| Departamento: |
Departamento de Estatística e Informática
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5124
|
Resumo: |
In order to understand global warming it is necessary to understand greenhouse gas emissions. Such motivation comes from the fact that these emissions significantly affect global warming, causing the destruction of the ozone layer. The present study aimed to develop a standard procedure for statistical analysis of the gases that have more influence on the greenhouse effect, such as nitrous oxide (N2O) and carbon dioxide (CO2). With this in mind, a bibliographic review of statistical procedures commonly used in this field of study was carried out. Among the most frequently used statistical methods are parametric tests (t-test and ANOVA), non-parametric tests (Kruskal-Wallis and Mann-Whitney) and generalized linear models (GLM). In addition, generalized linear mixed models (GLMM) were considered. Then, the advantages and disadvantages of each method were analyzed to identify the most appropriate one. Due to the non-normality of greenhouse gas emissions data, logarithmic transformation is often used to normalize them. Assuming that the data follows a log-normal distribution and that there are dependency over time in the analyzed measurements, it was noted that the GLMM presented better results. |
