[R] Can I do regression by steps?

[markleeds at verizon.net]

someone else can blast  me if this is not correct but i think that 2 
step procedure only gives the same answer as the regular regression if 
X1 and
X2 perfectly uncorrelated. If they are at all correlated, then what john 
pointed out messes the procedure up. i was asked that question on
an interview  a long time ago and  the question i always wondered but 
never asked  was why someone would want to do that ? so, here goes:
why do you want to do that ?




On Tue, Jul 8, 2008 at  6:25 PM, rlearner309 wrote:

> Thanks for the reply.
> I am awared of the difference, but can I do regression by steps at 
> all?  I
> am not feeling comfortable about it.
>
>
>
> John Sorkin wrote:
>>
>> Be very careful!
>> When regression is performed by steps, you often will not get the 
>> same
>> results as you would get from a single multivariable regression. The
>> explanation for this is not simple, but a simplified explanation is 
>> that
>> when you do your first regression,
>> y=f(x1)
>> all the total variance that can be accounted for is sucked up by x1
>> leaving little varinace to be accounted for by your second 
>> regression,
>> residuals=f(x2). In contrast when you perform a multivariable 
>> regression,
>> y=f(x1,x2) the total variance is proportioned between x1 and x2.
>> John
>>
>> John David Sorkin M.D., Ph.D.
>> Chief, Biostatistics and Informatics
>> University of Maryland School of Medicine Division of Gerontology
>> Baltimore VA Medical Center
>> 10 North Greene Street
>> GRECC (BT/18/GR)
>> Baltimore, MD 21201-1524
>> (Phone) 410-605-7119
>> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>>
>>>>> rlearner309 <unixunix99 at gmail.com> 7/8/2008 8:53 AM >>>
>>
>> I saw this type of models in some of my company projects.
>> To simplify:
>> Y is regressed on X1 and X2.  But the regression is done by two 
>> steps: First Y is regressed on X1 with intercept, and the residuals 
>> from the
>> first
>> step are used to regress on X2, without the constant.  The reason to 
>> do so
>> is some observations have X1 data but do not have X2, so I guess the
>> person
>> wants to use as much information as he can to get the coef. for X1, 
>> and
>> then
>> use part of the residuals (that have X2 data) to catch what is left 
>> to be
>> explained by X2.
>>
>> But my concern is, should we consider the correlation between X1 and 
>> X2? If
>> residuals from the first step are used, then X1 effect has been 
>> removed. Then what does it really mean by regressing residuals on X2, 
>> which has
>> some
>> X1 effect correlated with?? should X2 be adjusted by X1, too (regress 
>> X2
>> on
>> X1 and use the residuals)?
>> What if both X1 and X2 are dummy variables?  Dummy variables can have 
>> a
>> meaningful correlation, too, right?
>>
>> Thanks a lot!
>> -- 
>> View this message in context:
>> 
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>>
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>
> -- 
> View this message in context: 
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>
> ______________________________________________
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