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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