Kendall,
Are
there any definitive guidelines when using
hierarchical regressions in terms of what IV
variables should one put together in the same
block? Should one use IVs that are related by a posteriori
relationship? For example, should I group IV’s or
enter them into blocks by some relationship
between these type of variables? For example
personal income, liquid investments, retirement,
et. cetera?
Thank you,
Quentin
Zavala
SchoolsFirst
Federal Credit Union
Business Analyst, Research and Analytics
714-258-4000
ext 8601
qzavala[hidden email]
From: Art Kendall
[[hidden email]]
Sent:
Thursday,
May 24, 2012 3:03 PM
To:
Quentin Zavala
Cc:
[hidden email]
Subject:
Re: [SPSSX-L] SPSS OLS Regression to develop
Mortgage Model for a financial institution
Check legal requirements
for credit unions. Gender may be a "protected"
class like race and specifically illegal to use in
mortgage models to qualify applicants..
Also why not some kind of tree model.
In regression you might want to use some kind of
stepped (aka hierarchical) model but stepwise
approaches are notorious.
Also, by "may apply" do you mean "will be allowed
to" or "are likely to"?
Art Kendall
Social Research Consultants
On 5/24/2012
3:03 PM,
Quentin Zavala wrote:
Hello Rich,
Thank you for you
response. It gave me many good points to think
about…it is been seven years since I completed a
MS Thesis whereby I was using logistic and linear
regressions without a second thought. The
regional aspect of the formula for example doesn’t
apply to all regions (i.e., branches are basically
located in 4 separate counties in
California
therefore 99% of mortgages are in
California
within the 4 counties and this distribution is
skewed).
Let me declare I’m not a
statistician although my boss is and has 40 years
of Direct Marketing experience. He was tasked
with developing a Mortgage Model, so we can
determine what variables best predict what type of
members may apply for a mortgage. He told me to
use the stepwise method. But I’ll have to
investigate this method as you recommended.
To highlight the nature
of the IV regressed on the dichotomously coded
Mortgage variable; I used a binary (0, 1) or
(0=No, 1=Yes) to represent if a customer had a
current mortgage instead of the mortgage balance
range of (0 to 1,536,757) on 3.3% of the cases or
12,307. The independent variables used consisted
of ordinal and interval/ratio data. Basic
demographic variables were dummy coded; for
example male and female were separated out into
there own variables (0 = no, 1 = yes). Some of
the continuous variables were fico score, age
(continuous) and other products. The products
were dummy coded as well (0 = no participation, 1
= participation).
I first used the entire
sample or universe of cases. Then I used a 27%
random sample (using SPSS’s probability algorithm
in V. 19) and applied it to the full sample and
scored the data set using the regression equation
to create the gain chart.
What would you recommend
in terms what “Method of regression (Enter,
Stepwise, Remove Forward, Backwards) to use? Or
if Mortgage Bal should be used instead of binary
variables due to the fact I’m using linear
regression models.
Thank you in advance for
any further comments,
Quentin Zavala, MS
Quentin
Zavala
SchoolsFirst
Federal Credit Union
Business Analyst, Research and Analytics
714-258-4000
ext 8601
qzavala[hidden email]