RE: st: RE: RE: Insignificant coefficient in prediction

["Jeff" <jbw-appraiser@earthlink.net>]

Oops, Sorry. That email was meant to be a compliment and was intended
for a friend of mine.

Jeff



-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Jeff
Sent: Wednesday, December 01, 2010 3:06 PM
To: statalist@hsphsun2.harvard.edu
Subject: RE: st: RE: RE: Insignificant coefficient in prediction

This guy is an economist and statistician at Boston College.  That
doesn't get in the way of his insight.

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Christopher F
Baum
Sent: Wednesday, December 01, 2010 1:56 PM
To: statalist@hsphsun2.harvard.edu
Subject: re:st: RE: RE: Insignificant coefficient in prediction

<>
Thanks. I share with you the skepticism on significance. Let me ask this
way. Let's say a practitioner wants to find which factor has more impact
on
sales to make an investment decision: store size or good location. Let's
say, a regression model tells that the effect of store size is smaller
than
the effect of location but highly significant and that the effect of
location is larger but insignificant with p>0.5, for example. What
should we
recommend? To invest in store size or to pick up a better location? I
guess
he should invest in store size. Then, what implication does it have on
prediction using both coefficients? Are these two problems very
different
ones and should not be mixed? 


If your data cannot pin down the coefficient on location -- or even,
perhaps, its sign -- then you probably should respecify the model,
dropping that variable, and predict from the new model. But just because
the data cannot give you a precise estimate of the effect of location
does not mean it is irrelevant -- and there are probably other studies
that have found it important. You need to worry about how you have
measured location; it may be that some alternative measurement (or a
measure of location taking other factors into account) will give you
quite different results. For instance consider selling takeout food on
the northbound side of a divided highway leading out of Boston (such as
US Rte 1 north of the city). You have to drive 3-4 miles out of your way
to get to a store just across the road. Commuters pick up food on their
way home after work. A location on the east side of the highway (with
the flow of homebound traffic) might be expected to do much better than
the !
 same store just across the road, where it will not attract much of the
homebound commuting traffic. So how do you measure that 'good location'
vs 'bad location' factor? On the other hand a Dunkin Donuts or Crispy
Creme on the inbound side might be much more successful.

Nick and others, keep in mind that in the colonies we (usually)* drive
on the right side of the road, and don't have those pesky roundabouts
every mile on major highways.

Kit

* This is a general rule, and the general rule in Massachusetts
regarding driving is that people don't pay that much attention to
general rules.



Kit Baum   |   Boston College Economics and DIW Berlin   |
http://ideas.repec.org/e/pba1.html
An Introduction to Stata Programming   |
http://www.stata-press.com/books/isp.html
An Introduction to Modern Econometrics Using Stata   |
http://www.stata-press.com/books/imeus.html


*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/