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	Provedor de dados AgEcon (1) Autor Chen, Xiao (1) Mitchell, Michael N. (1) Palavra-chave Covariate contribution (1) Interactions (1) Logistic regression (1) Main effects (1) Predicted probabilities (1) Research Methods/ Statistical Methods (1) Mais... Tipo do documento Journal Article (1) Ano 2005 (1) País United States (1) Idioma Inglês (1)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 1 Primeira ... 1 ... Última Visualizing main effects and interactions for binary logit models AgEcon Mitchell, Michael N.; Chen, Xiao. This paper considers the role of covariates when using predicted probabilities to interpret main effects and interactions in logit models. While predicted probabilities are very intuitive for interpreting main effects and interactions, the pattern of results depends on the contribution of covariates. We introduce a concept called the covariate contribution, which reflects the aggregate contribution of all of the remaining predictors (covariates) in the model and a family of tools to help visualize the relationship between predictors and the predicted probabilities across a variety of covariate contributions. We believe this strategy and the accompanying tools can help researchers who wish to use predicted probabilities as an interpretive framework for... Tipo: Journal Article Palavras-chave: Logistic regression; Predicted probabilities; Main effects; Interactions; Covariate contribution; Research Methods/ Statistical Methods. Ano: 2005 URL: http://purl.umn.edu/117500 Registros recuperados: 1 Primeira ... 1 ... Última

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