Correlation

Hey Guys hows it going. Im seeking the best way to establish if there is a correlation between two columns

Could anyone offer any insight on how best to achieve this?

Please see the attachment - col b shows occupancy levels high to low and col c shows the cancellation rate.

i've never used the =correl before!

I was thinking that maybbe i should break the data up into rankings by percentile and use a scatter graph

Any help warmly recieved!

I don't know much about the subject but I can Google "correlation coefficient" ;-)

I came up with this:

http://mathworld.wolfram.com/Correla...efficient.html

Lots of formulae which I don't understand but a couple of clicks down you'll find some graphs. If I've interpreted them correctly, the closer the coefficient is to one (1), the closer the relationship between the arrays.

0.09 looks fairly scattered on the scale of things but it could be much worse.

Hope this helps

Regards

Hi TM - thanks for your input! ive been googling the living daylights out of it all morning!!

See below! im still stummped! Think i need one of Andys super scatter graphs!!

Cheers mate - will chk out the web site!

correlation coefficient
Definition
A statistical measure of the interdependence of two or more random variables. Fundamentally, the value indicates how much of a change in one variable is explained by a change in another. Used in portfolio and modelling.


Correlation

The CORREL and PEARSON worksheet functions both calculate the correlation coefficient between two measurement variables when measurements on each variable are observed for each of N subjects. (Any missing observation for any subject causes that subject to be ignored in the analysis.) The Correlation analysis tool is particularly useful when there are more than two measurement variables for each of N subjects. It provides an output table, a correlation matrix, that shows the value of CORREL (or PEARSON) applied to each possible pair of measurement variables.

The correlation coefficient, like the covariance, is a measure of the extent to which two measurement variables "vary together." Unlike the covariance, the correlation coefficient is scaled so that its value is independent of the units in which the two measurement variables are expressed. (For example, if the two measurement variables are weight and height, the value of the correlation coefficient is unchanged if weight is converted from pounds to kilograms.) The value of any correlation coefficient must be between -1 and +1 inclusive.

You can use the correlation analysis tool to examine each pair of measurement variables to determine whether the two measurement variables tend to move together — that is, whether large values of one variable tend to be associated with large values of the other (positive correlation), whether small values of one variable tend to be associated with large values of the other (negative correlation), or whether values of both variables tend to be unrelated (correlation near 0 (zero)).

EDIT - Email sent to mod to close this thread as i have created a new (similar one). Thanks guys