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Notice that the right hand side of the equation above looks like the multiple linear regression equation. In the following form, the outcome is the expected log of the odds that the outcome is present, The multiple logistic regression model is sometimes written differently. Is the expected probability that the outcome is present X 1 through X p are distinct independent variables and b 0 through b p are the regression coefficients. If we define p as the probability that the outcome is 1, the multiple logistic regression model can be written as follows: The outcome in logistic regression analysis is often coded as 0 or 1, where 1 indicates that the outcome of interest is present, and 0 indicates that the outcome of interest is absent. Hosmer and Lemeshow provide a very detailed description of logistic regression analysis and its applications. Here again we will present the general concept. Simple logistic regression analysis refers to the regression application with one dichotomous outcome and one independent variable multiple logistic regression analysis applies when there is a single dichotomous outcome and more than one independent variable. In essence, we examine the odds of an outcome occurring (or not), and by using the natural log of the odds of the outcome as the dependent variable the relationships can be linearized and treated much like multiple linear regression. The epidemiology module on Regression Analysis provides a brief explanation of the rationale for logistic regression and how it is an extension of multiple linear regression. Logistic regression analysis is a popular and widely used analysis that is similar to linear regression analysis except that the outcome is dichotomous (e.g., success/failure or yes/no or died/lived).