Hi Richard,

Looked like a late night for you...

>From what I remember of my stats at school, the correlation needed to be up

around 0.9 to 0.95 to be able to make any meaningful conclusions so 1 would

be a perfect fit. I don't have my A level notes to hand, unfortunately,

but from "Quantitative Methods" by Diana Bedward it is quoted as:

( sum(x*y) - n*mean_x*mean_y )/root((sum x^2 - n*mean_x^2)*(sum y^2) -

n*mean_y^2))

which follows from the least-squares method of doing the regression; if the

'means' coincide with the 'actuals' then the top and bottom of the equation

come out to be numerically the same. Note that a negative slope yields a

perfect correlation figure of -1.

Hope that helps,

Regards,

Mike Watts

---------------------------------------------------------------------

This is the "vrf" maillist, managed by Majordomo. To send messages to

this maillist, just email to "vrf@lvld.agilent.com". Subscriptions and

unsubscriptions are done through the address "vrf-request@lvld.agilent.com".

If you need details, just send a message containing the text "help"

to "vrf-request@lvld.agilent.com".

---------------------------------------------------------------------

Looked like a late night for you...

>From what I remember of my stats at school, the correlation needed to be up

around 0.9 to 0.95 to be able to make any meaningful conclusions so 1 would

be a perfect fit. I don't have my A level notes to hand, unfortunately,

but from "Quantitative Methods" by Diana Bedward it is quoted as:

( sum(x*y) - n*mean_x*mean_y )/root((sum x^2 - n*mean_x^2)*(sum y^2) -

n*mean_y^2))

which follows from the least-squares method of doing the regression; if the

'means' coincide with the 'actuals' then the top and bottom of the equation

come out to be numerically the same. Note that a negative slope yields a

perfect correlation figure of -1.

Hope that helps,

Regards,

Mike Watts

---------------------------------------------------------------------

This is the "vrf" maillist, managed by Majordomo. To send messages to

this maillist, just email to "vrf@lvld.agilent.com". Subscriptions and

unsubscriptions are done through the address "vrf-request@lvld.agilent.com".

If you need details, just send a message containing the text "help"

to "vrf-request@lvld.agilent.com".

---------------------------------------------------------------------

I thought it was something like

(1/N)*sum((Y_fit_data - Y_actual_data)^2)

but that becomes 0 for an exact fit. In Vee, a perfect fit is 1 or -1.

--

Regards,

Rich

===========================

Richard Kleinhenz

mailto:scubaman@us.ibm.com

845-892-2617

===========================

---------------------------------------------------------------------

This is the "vrf" maillist, managed by Majordomo. To send messages to

this maillist, just email to "vrf@lvld.agilent.com". Subscriptions and

unsubscriptions are done through the address "vrf-request@lvld.agilent.com".

If you need details, just send a message containing the text "help"

to "vrf-request@lvld.agilent.com".

---------------------------------------------------------------------