Para predizer a demanda por determinado tipo de serviço de...
Próximas questões
Com base no mesmo assunto
Ano: 2015
Banca:
CESPE / CEBRASPE
Órgão:
Telebras
Prova:
CESPE / CEBRASPE - 2015 - Telebras - Analista Superior - Estatística |
Q611982
Estatística
Para predizer a demanda por determinado tipo de serviço de comunicação de dados, um especialista em gestão de telecomunicações considerou um modelo de regressão linear múltipla na forma y = Xβ + ε, em que y é o vetor de respostas, X é a matriz de delineamento, β é o vetor de parâmetros, e ε denota o vetor de erros aleatórios independentes e identicamente distribuídos. Cada componente do vetor ε segue uma distribuição normal com média zero e variância v. O modelo ajustado é expresso por
, em que
representa a estimativa de máxima verossimilhança do vetor β.
Considerando que
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questão](data:image/jpeg;base64,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)
julgue o item que se segue.
O vetor de resíduos é dado por (I - H)y, em que H = X (X’X)-1 X’ é a matriz de projeção (hat matrix) e I é a matriz identidade.
Considerando que
O vetor de resíduos é dado por (I - H)y, em que H = X (X’X)-1 X’ é a matriz de projeção (hat matrix) e I é a matriz identidade.