> Quoting from the SysStat FAQ,
>
> > (1) In the presence of a constant, R^2 measures variation around the
> > mean of the dependent variable which is explained by the variation
> around
> > the mean of the independent variables.
> >
> > (2) Without a constant, R^2 measures variation arou
[EMAIL PROTECTED] wrote:
> I have found a difference between the results produced by SPSS and
> SYSTAT in linear regression with no constant term. Below are the
> results from the programs. As you can see the adjusted R2 given by the
> 2 programs is different. Which one is correct?
>
Quoting f
On Fri, 25 Feb 2000 11:09:53 GMT, [EMAIL PROTECTED] wrote:
>Thanks for the replies. It was the difference between the adjusted R2
>calculations that I was concerened about. I have also tried the
>calculations in SAS JMP and that does not output R2 or adj R2 for
>regressions without a constant t
Thanks for the replies. It was the difference between the adjusted R2
calculations that I was concerened about. I have also tried the
calculations in SAS JMP and that does not output R2 or adj R2 for
regressions without a constant term. Maybe this is a more appropriate
thing to do as it avoids
On Thu, 24 Feb 2000 12:45:29 GMT, [EMAIL PROTECTED] wrote:
> I have found a difference between the results produced by SPSS and
> SYSTAT in linear regression with no constant term. Below are the
> results from the programs. As you can see the adjusted R2 given by the
> 2 programs is different.
Rich Ulrich wrote:
>On Thu, 24 Feb 2000 12:45:29 GMT, [EMAIL PROTECTED] wrote:
>
>> I have found a difference between the results produced by SPSS and
>> SYSTAT in linear regression with no constant term. Below are the
>> results from the programs. As you can see the adjusted R2 given by the
>> 2
I don't disagree, Bob gives a useful elaboration. But I do think
the "multiple correlation coefficient" should at least be a correlation
coefficient, i.e. corr(Xb,y) maximized over b. In this case, that's
1.0. If you compute something else, call it something else.
At 1:03 PM -0600 2/24/00, Bob P
>
>The multiple correlation between X and Y does not depend on if there
>is a constant term in the regression or not. The residual sum of
>squares does. R2 is simply 1.0.
>
>Basically, the answer is: both are quite incorrect, or: only two decimals
>are correct.
Although I hate to pretend to corre
Without delving into the computational details, it seems to me that they are
both close enough that it doesn't seem to matter. It may be that *both* are
right, but just use slightly different algorithms. The differences don't
show up until about the 5th or 6th decimal place - that looks like rou
The multiple correlation between X and Y does not depend on if there
is a constant term in the regression or not. The residual sum of
squares does. R2 is simply 1.0.
Basically, the answer is: both are quite incorrect, or: only two decimals
are correct.
At 12:45 PM + 2/24/00, [EMAIL PROTECTE
I have found a difference between the results produced by SPSS and
SYSTAT in linear regression with no constant term. Below are the
results from the programs. As you can see the adjusted R2 given by the
2 programs is different. Which one is correct?
Data (from NIST line origin dataset):
Y X
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