You drive your new car for half a year : starting in cold spring, and while the car gradualy get's worn in, the temperatue rises and you improve your FE-driving skills, all improving FE on their own. Gas prices have risen continually, so there is your correlation. Wait for cold winter to arive and it'll be gone :-)

 

Yes, but I find it hard to ignore where the price drops as well as my FE. Im not saying that there are not other factors, but, thus far into my charting, it looks like price may be one of them.

I will have to find a temperature chart Omaha and see how that lines up with everything.

 

You know, that looks like a valid output from a correlation analysis. I wish you were in person and I had your dataset to play with because it would be a whole lot easier to explain.

Your number is positive, that means that your graph is skewing upward. If it were negative it means that the graph would be moving downward.

Let's limit your number to thousandths. That means it is 0.588

If the results of your correlation were higher in value, say .851 or .900, we would say that the results of your data set are highly correlated. If your results were .457 or .345 we would say that you have a weak correlation. You are about middle of the road. Meaning that right now, it is somewhat inconclusive if your observations are correlated to each other.

A few more tanks of fuel and we may see a change in the correlation, one way or the other. If the correlation value moves toward 1.00, then your conclusion is correct. If the correlation decreases further then you conclusion can't be supported, at least mathematically.

 

thanks for your help jerome. I would have never known about this. I will continue to watch this and I will post again after I have accumulated more data.

Ryan