PROPHET StatGuide: Logistic Regression


Logistic regression is used to fit a model to binary response (Y) data, such as whether a subject dies (event) or lives (non-event). These events are often described as success vs failure. For each possible set of values for the independent (X) variables, there is a probability p that a success occurs. The linear logistic model fitted by maximum likelihood is:
Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk
where Y is the logit transformation of p:
Y = log(p/(1-p)).


The full version of StatGuide for logistic regression will be available in a future release. In the meantime, to properly analyze and interpret results of logistic regression, you should be familiar with the following terms and concepts: If you are not familiar with these terms and concepts, you are advised to consult with a statistician. Failure to understand and properly apply logistic regression may result in drawing erroneous conclusions from your data. Additionally, you may want to consult the following references:

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Last modified: February 14, 1997

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