# 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:
- Agresti, A. 1990.
*Categorical Data Analysis.*
New York: John Wiley & Sons.
- Agresti, A. 1996.
*An Introduction to Categorical Data Analysis. * New York: John Wiley & Sons.
- Collett, D. 1991.
*Modelling Binary Data. *
London: Chapman and Hall.
- Cox, D.R. and Snell, E.J. 1989.
*The Analysis
of Binary Data. * 2nd ed. New York: John Wiley & Sons.
- Hosmer, D.W. and Lemeshow, S. 1989.
*Applied
Logistic Regression. * New York: John Wiley & Sons.
- McCullagh, P and Nelder, J.A. 1989.
*Generalized Linear Models.* 2nd ed. London: Chapman and Hall.

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##### Last modified: February 14, 1997

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