#r#, or the regression coefficient, is a simple value that is used when finding the closeness of a regression equation to the actual data points which it is trying to show a correlation between. #r# will range from #-1# to #1#. The closer the value for a regression equation/model is to #0#, the worse the model will be for showing a trend in the data. So a value closer to #-1# or #1# for #r# would therefore correspond to a more reliable and accurate equation/model to represent the data.
If you have a graphing calculator, such as a TI-#84#, then your math teacher should be able to help you to find an #r# value for a regression (or you can just look it up on www.youtube.com).
Just a note for the future: When you are trying to find #r#, a value may appear noted as #r^2#. This is basically the same concept as #r#, and they both show the closeness of a regression model/equation to the data which it tries to represent. The only difference is that #r^2# is #r# times #r#, or in standard English, the value of the regression coefficient squared.
I hope that helps!
