How to calculatate the correlation coefficient

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How to calculatate the correlation coefficient

Urs Held

Old Hand

Points: 349
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September 13, 2006 at 7:04 am

#153181

Hi
Who knows an elegant way to calculate the correlation coefficient of two series (columns) in T-SQL. It seems there is no aggregate function as there is one for the standard deviation.
Many thanks in advance
Urs

Viewing 6 posts - 1 through 5 (of 5 total)

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Sergei Zarembo Hall of Fame Points: 3887 More actions · Answer 1

b/c ther'es no standard function for this, u'd have to write your own function according to a well-known algorhithm. I'm sure it'll be elegant enough

Jeff Moden SSC Guru Points: 1004340 More actions · Answer 2

If the columns a VARCHAR or the like, you can use SOUNDEX (won't work on numbers).

--Jeff Moden

RBAR is pronounced "ree-bar" and is a "Modenism" for Row-By-Agonizing-Row.
First step towards the paradigm shift of writing Set Based code:
________Stop thinking about what you want to do to a ROW... think, instead, of what you want to do to a COLUMN.

Change is inevitable... Change for the better is not.

Helpful Links:
How to post code problems
How to Post Performance Problems
Create a Tally Function (fnTally)

Gift Peddie SSC Guru Points: 73570 More actions · Answer 3

There two Logs in the SQL Server math functions so that is a place to start float data type is required but you can see in the code sample you can convert to varchar. Hope this helps.

http://msdn2.microsoft.com/en-us/library/ms177516.aspx

Kind regards,
Gift Peddie

Tony Webster SSChasing Mays Points: 610 More actions · Answer 4

Implemented from the formula for the sample correlation coefficient on

http://en.wikipedia.org/wiki/Correlation (I'm assuming you want the linear

correlation coefficient).

Table called "data", columns "x" and "y":

select (count(*) * sum(x * y) - sum(x) * sum(y)) /

(sqrt(count(*) * sum(x * x) - sum(x) * sum(x)) *

sqrt(count(*) * sum(y * y) - sum(y) * sum(y)))

from data

I've tried it with some sample data, and the results look "reasonable" so

I think I've got the implementation right. It is a little numerically unstable

though - I got a value just a shade over 1 for a perfect linear fit.

Tony

Urs Held Old Hand Points: 349 More actions · Answer 5

Thank you very much to all of you. I implemented Tony's formula and I got exact the result what I was looking for. I will perform some more tests to check the stability for my secific data sets.

Urs