 So now we've explained kind of this whole Box and whisker, and if you didn't have any outliers, that's all that you would have But here we have these outliers. So then the next question is what does it mean to be an outlier now? This description of an outlier is somewhat arbitrary. We're doing somewhat of a heuristic here to say, okay What what would make a number outside the general range? We're we're going to determine it is an outlier remember outliers are really important because like For example if you were trying to determine the average wealth of somebody in a particular group and you had one Individual who happened to be a billionaire in it then that billionaire is going to pull up the average of The entire group it'll look like everybody's looks a lot more wealthy than they are Because that outlier could possibly skew the data Greatly depending on you know what the data set is looking at so we have to be careful of the outliers So enter quarter quartile range. This is the IQR. This is going to help us to determine the outliers We're gonna sit. We're gonna just take The quartile three minus quartile one. So this is going to help us to determine where they're coming up with these outliers So if we take quartile three minus quartile one quartile three is the seven two eight hundred minus quartile one six nine seven hundred The difference is that three thousand one hundred which we're calling the interquartile range the lower quartile I'll limit then is Quartile one minus the IQR the interquartile range times one port point five Which is somewhat arbitrary, right? So that's going to be our calculation though. So we're gonna say all right That's what it's calculating So now we're taking the q1 so q1 is Well, let's do it. Let's do the the enter part first the IQR Which is the three one oh oh times one point five Minus q1 minus q1 q1 minus six nine seven and that gives us our 6550 so 65,050 So we're on the lower limit. So anything around that's kind of over this line They're gonna add in a dot Format so that's going to be the outlier and then down here We have a similar calculation for that upper limit where we have the quartile three plus the IQR the interquartile range times one point five So if I take the IQR the three one times 1.5 and then and then I'm gonna add it this time to q3 so plus q3 7 to 800 that gives us our 77 450 which means that if anything's above like the 77 somewhere around here It's gonna put a dot on them. So that's why these two are out line There's the 55 outlier the 84 the 80 are gonna be on the other side 80 and 84 as outliers in our data alright, and then Just note that you in Excel. It's possible to like line up two box plots on on one chart So so if we had two sets of data similar sets of data basically We've just like added added a constant amount To the second data range and now just note that you can do a nice Visual comparison between the two sets of data right I can say okay This one compared to that one and we can get an idea of the averages and the medians and so on and what kind of outliers These ones happen to mirror each other very closely but just Staggered due to the fact that I created the second data list by just basically taking the first times like 1.1 Or something and then you can make your legend On the right-hand side. So notice when you get comparison of multiple data sets like this Then seeing it visually Will probably give you a much better understanding than even if you were to list your stats like you might say hey look to list those Two data sets. I'll just list my stats side-by-side. That's helpful But it's still a little bit hard to see right if you had these two kind of next to each other Then that gives you a pretty good representation About what's happening. I mean if I looked at for example if I looked at the stats side-by-side for the average the Q1 q2 q3 and so on and so forth I might not get an idea that it looks like that it looks like the data set has just been basically Multiplied by like 1.1, but if I look at this I Can say hey that looks pretty symmetrical And it and it moved up like the same and the outliers moved up in alignment with it You know you see you see how the pictorial representation could give you an idea Beyond the stats oftentimes or beyond the data set itself