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Hailpern, Susan M.; Visintainer, Paul F.. |
Logistic regression is perhaps the most widely used method for adjustment of confounding in epidemiologic studies. Its popularity is understandable. The method can simultaneously adjust for confounders measured on different scales; it provides estimates that are clinically interpretable; and its estimates are valid in a variety of study designs with few underlying assumptions. To those of us in practice settings, several aspects of applying and interpreting the model, however, can be confusing and counterintuitive. We attempt to clarify some of these points through several examples. We apply the method to a study of risk factors associated with periventricular leucomalacia and intraventricular hemorrhage in neonates. We relate the logit model to... |
Tipo: Journal Article |
Palavras-chave: Cc; Cci; Cs; Csi; Logistic; Logit; Relative risk; Case–control study; Odds ratio; Cohort study; Research Methods/ Statistical Methods. |
Ano: 2003 |
URL: http://purl.umn.edu/116084 |
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Thorat, V.A.; Dhekale, J.S.; Patil, H.K.; Tilekar, S.N.. |
The study has identified the factors responsible for rural-urban migration based on 120 sample respondents each of migrants and non-migrants spread over two districts, viz. Ratnagiri and Sindhudurg of Konkan region of Maharashtra by employing the logit model. The study has highlighted the importance of rural development programs like MGNREGA that are being implemented by the government with a view to provide employment and income to the rural population in the country. It has also shown that for both migrant and non-migrant households,, agriculture was the major source of income, and their consumption expenditure was more than the production expenditure. It has also been observed that migration has a positive impact on income, expenditure and net savings... |
Tipo: Article |
Palavras-chave: Migration; Logit; Variable inflation factor; Odds ratio; Agricultural and Food Policy; J11; J61; C13; R23. |
Ano: 2011 |
URL: http://purl.umn.edu/119399 |
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