¹Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Departamento de Medicina Social, São Paulo, Brasil. Pós-graduando. Email: eacbarros@gmail.com
²Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Departamento de Biomecânica, Medicina e Reabilitação do Aparelho Locomotor, São Paulo, Brasil. Pós-graduando. Email: priangelottisimoes@yahoo.com.br
³Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Departamento de Medicina Social, São Paulo, Brasil. Professor. Email: achcar@fmrp.usp.br
⁴Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Departamento de Medicina Social, São Paulo, Brasil. Professor. Email: edson@fmrp.usp.br
⁵Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Departamento de Biomecânica, Medicina e Reabilitação do Aparelho Locomotor, São Paulo, Brasil. Professor. Email: ashimano@fmrp.usp.br

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.

Palavras chave: regressão linear múltipla, mínimos quadrados, análise bayesiana, bootstrap.

In this paper, we show different parameters estimation forms for multiple linear regression model. We used clinical data, where the interest was to verify the relationship among the mechanical assay maximum stress with femoral mass, femoral diameter and group of ovariectomized Wistar rats. We used three inference methods: Classic inference, based on the least square method; bayesian inference, based on the Bayes theorem; and bootstrap inference, based on resampling processes.

Key words: Multiple linear regression model, Least square method, Bayesian inference, Bootstrap inference.

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