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Master's Dissertation
DOI
10.11606/D.55.2013.tde-05072013-161440
Document
Author
Full name
Marina Mitie Gishifu Osio
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2013
Supervisor
Committee
Noveli, Cibele Maria Russo (President)
Aoki, Reiko
Giampaoli, Viviana
Title in Portuguese
Análise de modelos de regressão multiníveis simétricos
Keywords in Portuguese
Dados educacionais
Distribuições simétricas
Modelos hierárquicos
Modelos multiníveis
Abstract in Portuguese
O uso de modelos multiníveis é uma alternativa interessante para analisar dados que estão estruturados de forma hierárquica, pois permite a obtenção de diferentes estimativas de parâmetros relativos a grupos distintos e, ao mesmo tempo, leva em consideração a dependência entre as observações em um mesmo grupo. Neste trabalho, desenvolvemos e aplicamos modelos de regressão multiníveis simétricos, a fim de fornecer alternativas ao modelo usual, sob normalidade. Além disso, apresentamos uma breve análise de diagnóstico e estudo de simulação. Como motivação, consideramos dados educacionais, a fim de avaliar se o número de reprovações no histórico escolar do aluno e a infraestrutura da escola são variáveis relevantes que afetam o baixo desempenho dos alunos do ensino básico na disciplina de Matemática
Title in English
Analysis of symmetrical multilevel regression models
Keywords in English
Educational data
Hierachical models
Multilevel regression
Symmetrical distribution
Abstract in English
The use of multilevel models is an interesting alternative to analyze data that is structured in a hierarchical manner, since it allows the obtention of different parameters estimates for distinct groups and, at the same time, it takes into account the dependence of observations in the same group. In this dissertation, we develop and apply symmetrical multilevel regression models, for the purpose of providing alternatives to the usual model, under normality. Furthermore we present a brief diagnostics analysis and a simulation study. As motivation, we consider educational data in order to assess whether the number of failures in school history of students and the school infrastructure are important variables that affect the low performance of elementary school students in Mathematics
 
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Dissertacao_Marina2.pdf (727.98 Kbytes)
Publishing Date
2013-07-10
 
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