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
(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
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 –
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