Regression difficulty

When doing a regression analysis I am constrained with the
use of only 16 explanitory variables. Does anyone know
anyway around this?

Thanks,
Zak

Use a dedicated stats package, such as S-Plus (Windows-only) or R
(open-source, free equivalent of S, available also in compiled form for
many platforms. There are certainly other packages, but those are my
favorites, in that they are not exactly dedicated to stats, but rather
object-oriented environments that are stat-focused, so to speak.

For R, which I highly recommend, see SourceForge.

In the future, consider including the version of Excel you have in mind,
as the stat functionality has changed quite a lot in the last couple of
versions.

HTH,
Dave Braden
MVP - Excel

In article ,
"Zak" wrote:

When doing a regression analysis I am constrained with the
use of only 16 explanitory variables. Does anyone know
anyway around this?

Thanks,
Zak

--
(ROT13)

In message , Zak
writes
When doing a regression analysis I am constrained with the
use of only 16 explanitory variables. Does anyone know
anyway around this?

Excel's original built in regression functions only just work reliably
for the case of 3 explanatory variables for ill conditioned problems.
You are crazy if you use it for anything where getting the right answer
actually matters.

The MS mantra on statistics is that "business users do not care".

Look for previous threads comparing the performance of the spreadsheet
function linear regression against the chart polynomial fit for more
details.

It is claimed that they have finally improved the situation in Office XP
but I haven't got around to testing it. Perhaps others have?

Use a decent statistical package instead and even then always check that
the notional fit it computes really is a true least squares solution!

Regards,
--
Martin Brown

Martin,
I agree that the MS mantra you cite has been true since Excel first
appeared (on a Mac, at that); that mantra is being redefined, so to
speak. Excel 11 has marked improvement in LINEST, TREND, SLOPE, and
such, as well as the VAR-related functions. Some of the probability
functions (such as those related to the Normal) are much better, indeed
IMHO acceptable for serious work.

Several of us have put these functions through the wringer. As you know,
I am hardly an MS toady, yet can honestly write that, to their credit,
MS has really worked hard to address some of the stat shortcomings that
you, others and I have been long pointing out.

To get a sense of where MS's mindset has come, check out
http://support.microsoft.com/?kbid=828888

If you concede that the company is loathe to admit problems with Office,
then you must admit that this KB article (sheesh, site) says a lot about
what's being done "behind the scenes"

As for your suggestion re using a decent stat package, I couldn't agree
more. For critical stuff, I always use at least two packages. R is
really coming along at a fast clip, and is free.

Regards,
Dave Braden
MVP - Excel

In article ,
Martin Brown wrote:

In message , Zak
writes
When doing a regression analysis I am constrained with the
use of only 16 explanitory variables. Does anyone know
anyway around this?

Excel's original built in regression functions only just work reliably
for the case of 3 explanatory variables for ill conditioned problems.
You are crazy if you use it for anything where getting the right answer
actually matters.

The MS mantra on statistics is that "business users do not care".

Look for previous threads comparing the performance of the spreadsheet
function linear regression against the chart polynomial fit for more
details.

It is claimed that they have finally improved the situation in Office XP
but I haven't got around to testing it. Perhaps others have?

Use a decent statistical package instead and even then always check that
the notional fit it computes really is a true least squares solution!

Regards,

--
(ROT13)