Dear List members,
I am studying customers' repatronage decisions using logistic regression
and
tobit.
I analysed a data set of 516 observations (516 customers' purchasing
history
and survey measures) using 10 fold cross-validation and logistic
regression.
To validate the logistic regression results, the logistic regression
classifiers were trained on 9 folds and tested on 1 fold. This was
performed 10 times, each time using other training and testing folds.
Hence, I obtained 10 p-values and coefficients for each input.
With this procedure I averaged the several clasification matrix to
obtain a
single meausre (as recomended in Hair et al., p. 275). However, my
problem
is that I want to aggregate these 10 p-values and coefficients into one
measure of input significance. Unlike the classification matrix, I have
the
feeling that I can not average these p-values.... but I am not sure.
Do you have any suggestions how I can do this? For marketing purposes
the
classification accuracy is important, but the p-values (and log-odds)
are as
important because they will allow me to test different hypotheses.
I thank you for any literature reference or idea on how I can solve this
problem.
--
Remove the string "dot" from e-mail address.
=================================================================
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
http://jse.stat.ncsu.edu/
=================================================================
