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
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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