Date: Fri, 23 Sep 2005 19:47:45 +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"
Yes, I know that. But why does is go lower then 1 even if it was never
trained for that? I am just wondering and would like to know curiously-wise.
Not becuase it is a major problem for me... Perpaps you have an idea? Or
perhaps there is not general explaination and it depends on the model
itself?
Karl
> --- Ursprüngliche Nachricht ---
> Von: Hector Maletta <hmaletta@fibertel.com.ar>
> An: SPSSX-L@LISTSERV.UGA.EDU
> Betreff: Re: Regresssion Plot in SPSS
> Datum: Fri, 23 Sep 2005 13:52:52 -0300
>
> Karl,
> Since the coefficients of A and C are negative, the predicted value when A
> and C are at their maximum is actually the MINIMUM predicted value, in
> this
> case 0.967 which is close to the minimum observed value of 1. When A and C
> are at their minimum value of 0, the predicted value of Y would equal the
> constant, 4.16, which is less than the maximum value of Y but within the
> permissible range of 1 to 6. Other combinations would give predicted
> values
> between 0.967 and 4.16.
>
> Hector
>
>
>
> > -----Original Message-----
> > From: Karl Koch [mailto:TheRanger@gmx.net]
> > Sent: Friday, September 23, 2005 1:42 PM
> > To: Hector Maletta; Mailinglist SPSS
> > Subject: RE: Regresssion Plot in SPSS
> >
> > 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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