Learning Objectives
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1.
Regression with log-link is useful for studying the relative change in an outcome variable.
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2.
In a log-link regression model, the antilog of each coefficient represents the independent association of that covariate with the relative change in the outcome variable, holding all other variables constant.
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3.
Logistic regression is useful for studying associations for a binary outcome variable.
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4.
In a logistic regression model, the antilog of each coefficient represents the odds ratio of that covariate with the outcome variable, holding all other variables in the model constant.
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5.
In log-link and logistic regression models, the null hypothesis for a covariate is that the antilog of the coefficient for that covariate equals 1.0.
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© 2009 Springer Science+Business Media, LLC
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Kestenbaum, B. (2009). Non-Linear Regression. In: Epidemiology and Biostatistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-88433-2_19
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DOI: https://doi.org/10.1007/978-0-387-88433-2_19
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