So we're looking at the data and we can see that the data goes from 27 all the way up to 34. And if we look at the history, Graham Instagram, uh, looks to be, you know, pretty bell shape. So we would think that this is approximately a normal distribution. Then part B were to look at the normal Quanta will plot and remember, for a normal quantum plot, you're probably not ever asked to make one long hand. I never had my students do that. But you are looking at those values and you're hoping that you're seeing some linearity and so approximately linear. Therefore, the tendency is for the distribution to be approximately normal, not perfect, but not too bad. Then we want to look at on part, see what the inter quartile ranges and, uh, we need We have 50 numbers, so if you have them all listed down, um, if you put them all on your calculator, your calculator will give it all to you with 11 should put one variable stat. But if you have 50 numbers and then you need to count to find the 25th number 26 number, and that number will be your median and that number ends up coming out to be 30. And then we have 25 numbers who are below and so 25 numbers below. If I take 25 divide it by two, I get 12.5. So there are 12 numbers here, 12 numbers there. We need the 13th number in the list counting this way, and we also will need the 13th number counting this way, this one will be our Q one and Q one comes out to be 29 and therefore Q three counting comes out to be 31. And so our inner core tell range is the difference between Q three and Q one is two. And so, in order to find where those whether we have outliers or not, we want to take the Q one, and we want to subtract away 1.5 boxes. So 29 minus three anything below 26 is going to end up being an outlier, and there are none. No low outliers, and then we need to take Q three and add on 1.5 boxes, box wits or recurs. So 31 plus three is 34 anything higher than 34 is going to be an outlier. And our highest number is 34. So there are none. There are no outliers. Yeah, we don't count those that are right at that limit. And then we need to find the Pearson uh, index. And there are two methods for finding that one is the ski Eunice, where we take the mean and subtract away the mode and then divided by the standard deviation. And I have that the, uh I mean, when I calculated was this my mode most frequently occurring number was 30 and then dividing it by the standard deviation which I had had that day to put in my calculator. And I get this as the scariness. So it is positive it's pretty close to zero. So if it's skewed, it's just a little bit skewed to the right. And if we use the second index formula, that's three times we take the mean minus the median divided by the standard deviation. And in that case, it ends up that the median and the mode are the same. So I just finished writing here. So basically three times that quantity that we had up here and this Kunis number would be 30.265 So again, if there is Kunitz, it's skewed just a little bit to the right. And so it appears that this distribution is pretty close to being a normal distribution. Not too bad. You can tell that really from the 22 earlier plots that there's not too much skin this.