Date: Fri, 23 Sep 2005 18:41:35 +0200
Reply-To: Karl Koch <TheRanger@gmx.net>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Karl Koch <TheRanger@gmx.net>
Subject: Re: Regresssion Plot in SPSS
Content-Type: text/plain; charset="iso-8859-1"
Hello all,
I have now a regression model that does exclude the non-significant second
factor (B) as it was suggested earlier on this discussion. To recall the
earlier discussion, I had three factors A, B, and C. B, however turned out
to be non-significant. The final regression (non-standarized) equation looks
like now like this:
Y = 4.16 - 0.778 A - 1.637 C
The original scale of the dependent variable (Y) is contineous and but
limited between 1.0 and 6.0. The following list is the range of values for A
and C:
A = {0,1,2}
C = {0,1}
However when using the equation with the most extreme values for A (=2) and
C (=1) I get a result of 0.967. This value is lower than the minimum value
of the orginal scale of the dependent variable. I am wondering why this
is... The prediction should actually not go below the theoretical minimum of
the scale especially since this was never learned from previous data. Is
this the appearance of some sort of error (espsilon) ?
Can somebody explain that?
Kind Regards,
Karl
> --- Ursprüngliche Nachricht ---
> Von: "Hector Maletta" <hmaletta@fibertel.com.ar>
> An: "'Karl Koch'" <TheRanger@gmx.net>, <SPSSX-L@LISTSERV.UGA.EDU>
> Betreff: RE: Regresssion Plot in SPSS
> Datum: Wed, 21 Sep 2005 17:05:28 -0300
>
> Karl,
> As you can see from your own results, standardized coefficients do not add
> up to 1. Each standardized coefficient (beta) is the expected change in Y,
> measured in standard deviations, for each increase of one std deviation in
> X. There is no reason for these coefficients to add up to 1.
> The advantage of beta over b is simply that they are comparable to each
> other, and across studies, because they are independent of the unit of
> measurement and the distributional peculiarities of each variable, since
> all
> are measured in each variable's own standard deviation units. On the other
> hand, they are relative to the std deviation and are less understandable
> for
> readers, who may understand better the raw b coefficients (so much
> increase
> in Y for each unit increase in X, both measured in their natural units).
>
> Hector
>
> > -----Original Message-----
> > From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU]
> > On Behalf Of Karl Koch
> > Sent: Wednesday, September 21, 2005 4:40 PM
> > To: SPSSX-L@LISTSERV.UGA.EDU
> > Subject: Re: Regresssion Plot in SPSS
> >
> > The standardized coefficients would then be what I am looking
> > for. I need a parameter that adds up to 1.0 which I can
> > directly assign to the independent variables.
> >
> > Karl
> >
> > > --- Ursprüngliche Nachricht ---
> > > Von: Vishal Dave <VishalDave@Affina.com>
> > > An: SPSSX-L@LISTSERV.UGA.EDU
> > > Betreff: Re: Regresssion Plot in SPSS
> > > Datum: Wed, 21 Sep 2005 13:53:29 -0500
> > >
> > > Karl,
> > >
> > > Since your variables are not standardized, the coefficients
> > won't add
> > > up to 1.0. You have to covert the variables in
> > standardized variables
> > > and
> > then
> > > run the regression model. It will give you model with
> > intercept = 0
> > > and
> > > sum(coefficients) = 1. This will also take care of having
> > different
> > > non-standardized variables with different mean and SD.
> > >
> > > Vishal.
> > >
> > >
> > >
> > > -----Original Message-----
> > > From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU]
> > On Behalf
> > > Of Karl Koch
> > > Sent: Wednesday, September 21, 2005 12:48 PM
> > > To: SPSSX-L@LISTSERV.UGA.EDU
> > > Subject: Re: Regresssion Plot in SPSS
> > >
> > > So, what you are saying is I have to create first a new regression
> > > model that only includes A and C. Then only use the unstandarized
> > > regression coefficients. Did I understand you correctly?
> > >
> > > What is the difference to the standarized coefficients? Do the
> > > standarized coefficients somehow add up to 1.0 so that they can be
> > > seen as percentage of magnitiude?
> > >
> > > Kind Regards,
> > > Karl
> > >
> > > > --- Ursprüngliche Nachricht ---
> > > > Von: "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
> > > > An: SPSSX-L@LISTSERV.UGA.EDU
> > > > Betreff: Re: Regresssion Plot in SPSS
> > > > Datum: Wed, 21 Sep 2005 12:35:19 -0500
> > > >
> > > > No, you are using the unstandardized intercept but the
> > standardized
> > > > regression coefficients. And because B may bias the weights for A
> > > > and C,
> > > you need
> > > > to run the regression again without B. The coefficients
> > for Aand C
> > > > will likely change. Then use the unstandardized
> > coefficients for the
> > > regression
> > > > equation.
> > > >
> > > >
> > > > Paul R. Swank, Ph.D.
> > > > Professor, Developmental Pediatrics
> > > > Director of Research, Center for Improving the Readiness
> > of Children
> > > > for Learning and Education (C.I.R.C.L.E.) Medical School
> > UT Health
> > > > Science Center at Houston
> > > >
> > > > -----Original Message-----
> > > > From: Karl Koch [mailto:TheRanger@gmx.net]
> > > > Sent: Wednesday, September 21, 2005 10:36 AM
> > > > To: Swank, Paul R; Mailinglist SPSS
> > > > Subject: RE: Regresssion Plot in SPSS
> > > >
> > > > Hello Paul,
> > > >
> > > > Yes, I agree. Given the fact that now I only have two
> > paramters left
> > > > (=being significant), is this the correct equation
> > (keeping in mind
> > > > the
> > > table
> > > > below)?
> > > >
> > > > Y = 4.195 - 0.319 * A - 0.442 * C + e
> > > >
> > > > What would be e in this case? I have standard erros for each
> > > parameter...
> > > >
> > > > Kind Regards,
> > > > Karl
> > > >
> > > > ----------------------------------------------------------------
> > > > Model Unstd. Coeff. Std. Coeff. t Sig.
> > > > B Std. Error Beta
> > > > -----------------------------------------------------------------
> > > > 1(Constant) 4.195 .070 58.971 .000
> > > > A -.779 .042 -.319 -18.278 .000
> > > > B -.028 .046 -.010 -.618 .534
> > > > C -1.635 .064 -.442 -25.354 .000
> > > > -----------------------------------------------------------------
> > > >
> > > >
> > > > >
> > > > > Drop B from the model first as it could bias the
> > parameters for A
> > > > > and
> > > C.
> > > > >
> > > >
> > > > --
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