Logistic regression is a widely used technique in database marketing for creating scoring models and in risk classification . It helps develop propensity to buy, and propensity to default scores (and even propensity to fraud ) .

This is more of a practical approach to make the model than a theory based approach.(I was never good at the theory ;) )

If you need to do Logistic Regression using SPSS, a very good tutorial ia available here

http://www2.chass.ncsu.edu/garson/PA765/logistic.htm

(Note -Copyright 1998, 2008 by G. David Garson.

Last update 5/21/08.)

For SAS a very good tutorial is here –

SAS Annotated Output

Ordered Logistic Regression. UCLA: Academic Technology Services, Statistical Consulting Group.

from http://www.ats.ucla.edu/stat/sas/output/sas_ologit_output.htm (accessed July 23, 2007).

For R the documentation (note :Still searching for R ‘s Logistic Regression ) is here

http://lib.stat.cmu.edu/S/Harrell/help/Design/html/lrm.html

–

lrm(formula, data, subset, na.action=na.delete, method=”lrm.fit”, model=FALSE, x=FALSE, y=FALSE, linear.predictors=TRUE, se.fit=FALSE, penalty=0, penalty.matrix, tol=1e-7, strata.penalty=0, var.penalty=c(‘simple’,’sandwich’), weights, normwt, …)

For linear models in R –

http://datamining.togaware.com/survivor/Linear_Model0.html

An extremely good book if you want to work with R , and do not have time to learn it is to use the GUI

rattle and look at this book

Informative article, exactly what I needed.

It can be useful to add a Prediction-versus-Actual graph