 and the standard deviation or spread calculation for those two numbers, then we'll do our mathematical calculations, same kind of thing we did before. We have the batting average, we've got the z-score, same concept with the z-score. Now the z-score might not be exact the same because we might have trimmed off a little bit more or less in terms of the very high ones and the very low ones, but the calculation of the z for the data that we have here is going to be the same kind of concept, 0.311 each x minus the mean 0.221 divided by the z 0.06 and that's going to give us our 1.5 about. We could do that all the way down. We do the same thing for the RBI's which are absolute numbers here, so now we're going to say all right the RBI's are going to be the 131 minus the mean on the RBI's 26.575 and then take that and divided by divided by the standard deviation 26.337 gives us about 3.95 about. We could do that all the way down, all the way down. We can multiply out the z's and see what that does 1.518 times the 3.965 gives us the 6.018. Okay we can do that all the way down and then if we sum out that outer column that gives us our numerator. So we sum that up that gives us our numerator 35289. The denominator is going to be n minus 1 so we can then take the number of data points in this case 823 minus 1 that gives us 822 and then we have the numerator and denominator in the outer columns. So I can then say all right this is 352.898 divided by 822 and we get .429 so that's more highly correlated. I can do the same thing with my data analysis tool to double check it and that looks good and then if I plot this I can say okay now we've got something there's our regression line our trend line and it looks obviously more positively correlated here so we're going to say all right yeah the batting average as the batting average goes up then the RBI's goes up that's kind of what we would expect. We have here the batting average on the x and that's usually what we would do if we think that is kind of like the independent and then the dependent over here the RBI's but we could reverse it and it would look like this so now you've got it's still positively a positively sloping line but now we've got the RBI's and actually if you think about like the RBI's would be a would be a decent way to then think about what the batting average would be right according to this right if we knew the RBI's we might be able to predict you know what the batting average is now notice there's going to be of course outliers on these so like this one over here we had a batting average of higher than 0.6 between points you know 0.65 or something that's a very high batting average what would that indicate that would if they're that high of a batting average it would indicate that they didn't get on base I mean I'm sorry it would indicate that they didn't have that many at bats they probably had a few at bats which means the ratio isn't really a valid ratio possibly we could we might have been better off to delete that one because they probably didn't have that many at bats which means they didn't get and you can kind of see that from here because they if they had a really high batting average you would think they would have batted somebody in right and they didn't now maybe they were the lead-off hitter or something like that so they didn't have any opportunity but still as the lineup turns over you would think that they would have opportunities to bat someone in typically so something seems kind of funny with that if you flip it over here you get the same thing but now here's that here's the dot up here at the 6.5 and now the RBI's are down below