Correlation coefficient Explained!

In a posting yesterday (More [and more technical] Info) I challenged Steve Watson to explain the term "correlation coefficient" so that everyone would understand. He's done it! At least I think I finally understand it.

Hi, Marie,

A correlation coefficient simply indicates whether or not there is a relationship between two datasets. In this case I used the simplest one, denoted r, which indicates whether or not there is a *linear* relationship. A value of 1 would indicate a perfect linear relationship, while a value of -1 would indicate a perfect linear relationship with negative slope (as one value goes higher, the other goes lower). 0 indicates no linear relationship at all. The closer the value is to 1 or -1, the more closely the two datasets show a linear relationship.

The thing to watch out for is that this only indicates the presence or absence of a linear relationship...the two datasets could be related in many other ways (non-linear polynomial, for example) and this test would not show that.

Also, it's of utmost importance to remember the phrase "correlation does not imply causation"! This was just a test to see if such a simple relationship *might* exist.