 Statistics and Excel. Normal distribution calories example part number two. Get ready, taking a deep breath, holding it in for 10 seconds, looking forward to a smooth soothing Excel. Here we are in Excel. If you don't have access to this workbook, that's okay because we basically built this from a blank worksheet, but we started in a prior presentation, so if you want to start from a blank worksheet, you may want to begin back there. However, if you do have access to this workbook, there's three tabs down below. Example, practice blank. Example, in essence, answer key. Practice tab, having pre-formatted cells so you can get to the heart of the practice problem. Blank tab is where we started with a blank worksheet, having just our beginning data on it, so we can practice formatting the cells within Excel as we work through the practice problem, and we're going to continue on with that now. Quick recap of what we have done thus far. We had our data on the left. We had our calorie counts, which were counted by day. We made a table out of it so we can sort by date or by calories if we so choose. We calculated the mean or average standard deviation, median mode, the mean being close to the standard deviation, as well as graphing the actual data and having it look somewhat like a bell curve as well as just basically having an assumption about the types of data being calorie counts, which you would assume would probably hover around a center point. Otherwise, you would think someone would get heavier or lighter over a time frame and would give us an indication that a bell curve might be something that would be useful. We then wanted to plot the bell curve. Now to do so, we took four standard deviations below and above, and then we ran into the issue of do I really want to plot one calorie at a time? If we do that, it would be great because then our percentages over here would basically add up to 100% about because we have four standard deviations, and that's nice. But if we go four standard deviations below, we end up with these negative numbers, which is something that is impossible in actual practice because you can have the negative calories. And also, we end up with very small units of data. So when I plot this out, we get to these small percentages. And if we compare that then to our actual count, then we can compare the actual count in terms of we can take these percentages times the count, meaning the number of calorie counts in days that we did, and it's going to be difficult to make a comparison between that and the actual data because again, the actual data is going to have to be when each of these calorie counts will have to be a whole number instead of this fraction of a number. So whether we do it by whole numbers or by or trying to do the count or by a percent, we end up with this issue. So we have to basically the next thing we want to do is say, OK, let's add some buckets and see if we can do this with our buckets. And also, just note that if we were to plot our actual data, like if I tried to plot my actual data as we have seen in the past on top of this graph, it's not going to work quite well because I have too much, you know, there's going to be a lot of blank space in the actual data. So if I tried, so this is in terms of percentages, you can see if I tried to say, I'm just going to add my actual data in terms of percentages on top of it as we've seen in prior presentations to try to see the comparison between the two. We're going to run into an issue. Let's just show that charts. We're going to go and say select data and I'm going to say, OK, let's add the percent of total this column. I'm going to delete what's in here thus far, hit this little button and then select from here, control shift down and enter and then OK. So there we have it. I'm going to say OK. And so now if I scroll on back up, we're going to see, so now it gets all messed up, right? And that's because, again, we have this issue with the too many units of data, so it's not going to line up. Let's undo that. And before I continue, I should have changed the X's down here last time. So let's do that now. I'm going to go to the chart design data and let's go to the edit and select this item. And I want to pick up my calorie counts here on the X's shift up so I don't pick up the total and OK. So there it looks like it's picking it up. I'm going to say OK. So now we have it goes on down to the zeros, obviously in practice it's going to stop at zero because we're not going to have those negative numbers. But if you want to see the full bell curve, it's going to give you that information. Now the other thing to just keep in mind is remember that because we had so many counts, we have pretty slim slices of data. So when you think about like integral calculus, usually you think of it as an infinite area of these lines, right? Well, we're getting pretty close if we were to sum up all of these lines because we have such small amounts, right? So we'll take a look that'll come into play possibly shortly here. So now let's say that we're going to say let's make a skinny L and say, alright, how can we kind of compare this data? I'm going to pull this to the side, get it out of the way, get out of my way, man. I'm doing stuff here. I'm doing stuff here. Let's make an X and a Y or let's make an X and then let's say that the X is going to be from zero to let's say like 400, 400. And these will be kind of our buckets and then to 800 and so on. And I'm going to go, I'm going to select those three, control or the fill handle and bring it down to 5600, our upper limit. So it brings it down to 5600 because that's our upper X so it's greater than our...