Hi everyone,

You have a set of data in one row or one column.

Next, you want to do nonlinear regression analysis for forecasting
purposes and you use LINEST.

The question is:
Should someone keeps dancing back-and-forth inbetween different
nonlinear polynomials without knowing which one is more accurate? I
mean how to do you measure the inaccuracy level?

Any advice or feedback on this please?

Thanks,
Jo

What is the source of the data? Is accuracy really an issue here?
Be aware that if you have n data points (say 4) then you will always get a
exact fit with an n-1 (third) order polynomial
The quantity to check is R2 - look in Help to see where LINEST reports this.
R2 value of 1 indicates a 'perfect' fit; Now I await Jerry Lewis's comment
<gr
best wishes
--
Bernard V Liengme
Microsoft Excel MVP
www.stfx.ca/people/bliengme
remove caps from email

"Jo" wrote in message
ups.com...
Hi everyone,

You have a set of data in one row or one column.

Next, you want to do nonlinear regression analysis for forecasting
purposes and you use LINEST.

The question is:
Should someone keeps dancing back-and-forth inbetween different
nonlinear polynomials without knowing which one is more accurate? I
mean how to do you measure the inaccuracy level?

Any advice or feedback on this please?

Thanks,
Jo

On Aug 9, 11:40 am, "Bernard Liengme"
wrote:
What is the source of the data? Is accuracy really an issue here?
Be aware that if you have n data points (say 4) then you will always get a
exact fit with an n-1 (third) order polynomial
The quantity to check is R2 - look in Help to see where LINEST reports this.
R2 value of 1 indicates a 'perfect' fit; Now I await Jerry Lewis's comment
<gr
best wishes
--
Bernard V Liengme
Microsoft Excel MVPwww.stfx.ca/people/bliengme
remove caps from email

"Jo" wrote in message

ups.com...



Hi everyone,

You have a set of data in one row or one column.

Next, you want to do nonlinear regression analysis for forecasting
purposes and you use LINEST.

The question is:
Should someone keeps dancing back-and-forth inbetween different
nonlinear polynomials without knowing which one is more accurate? I
mean how to do you measure the inaccuracy level?

Any advice or feedback on this please?

Thanks,
Jo- Hide quoted text -

- Show quoted text -

So, if I have 10 data points, I should consider 9th polynomial? How r2
is calculated? I read MS help but it is not that clear on it!

If you have 10 data points a ninth-order polynomial will give R2=1
But that does not tell you very much - it is just a mathematical thing; you
need to consider the meaning of you data. Generally, unless you KNOW
beforehand that the data should fit a certain polynomial there is not much
to gain by just getting a fit to a high-order equation!
How is R2 computed? You should Google or read a stats book
best wishes

--
Bernard V Liengme
Microsoft Excel MVP
www.stfx.ca/people/bliengme
remove caps from email

"Jo" wrote in message
oups.com...
On Aug 9, 11:40 am, "Bernard Liengme"
wrote:
What is the source of the data? Is accuracy really an issue here?
Be aware that if you have n data points (say 4) then you will always get
a
exact fit with an n-1 (third) order polynomial
The quantity to check is R2 - look in Help to see where LINEST reports
this.
R2 value of 1 indicates a 'perfect' fit; Now I await Jerry Lewis's
comment
<gr
best wishes
--
Bernard V Liengme
Microsoft Excel MVPwww.stfx.ca/people/bliengme
remove caps from email

"Jo" wrote in message

ups.com...



Hi everyone,

You have a set of data in one row or one column.

Next, you want to do nonlinear regression analysis for forecasting
purposes and you use LINEST.

The question is:
Should someone keeps dancing back-and-forth inbetween different
nonlinear polynomials without knowing which one is more accurate? I
mean how to do you measure the inaccuracy level?

Any advice or feedback on this please?

Thanks,
Jo- Hide quoted text -

- Show quoted text -

So, if I have 10 data points, I should consider 9th polynomial? How r2
is calculated? I read MS help but it is not that clear on it!

While R^2 would be 1, that fit will ususually be totally useless for
forecasting. Often a more useful measure is "adjusted R^2"
http://en.wikipedia.org/wiki/Coeffic...on#Adjusted_R2
which includes a penalty for increasing model complexity without an adequate
corresponding increase in explanatory value.

Jerry

"Jo" wrote:

So, if I have 10 data points, I should consider 9th polynomial? How r2
is calculated? I read MS help but it is not that clear on it!