 Statistics and Excel. Histogram examples. Got data? Let's get stuck into it with statistics and Excel. You're not required to, but if you have access to OneNote icon, left-hand side, OneNote presentation, 1070 Histogram Examples tab. Also, we're uploading transcripts to OneNote so you can use the immersive reader tool. Change the language. First, a word from our sponsor. Well, actually these are just items that we picked from the YouTube shopping affiliate program, but that's actually good for you. Because these aren't things that were just given to us from some large corporation which we don't even use in exchange for us selling them to you. These are things that we actually researched, purchased, and used ourselves. Bayer Dynamic? Not sure if I said that right, but this is the DT770 Pro 250 OHM Studio Reference Closed Back Headphones. I wear headphones basically every day for a large part of the day. They are important to me. 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We can get data from many, many areas and applying a pictorial representation such as a histogram can be an applicable step in a lot of those different areas. And two, we want to get a feel of just the different the different looks that a histogram can look like with different data sets. So let's just go through some histograms here. So this is going to be the steps. So we're going to imagine here that the data being pulled was the number of steps that were taken. So we've got 00 and then the data set going up here. We've sorted it from lowest to highest. We have a very long data set for the number of steps that have been tracked. So if we make a histogram from this, now we've got the steps put into buckets. So we've made the buckets 0 to 25,000, 25,000, 5,000, 5,000, 7,500, 7,500 to 1,000. And we get this pictorial representation of the steps because we see how many of the count fall into those particular those buckets. Now these are going to be basically skewed to the right because we have the tail end to the right. So we have very few days where we had steps of 27,500 to 3,000. So those were big exercise days. And after that day happened, possibly the next day, we didn't have that many steps. Well, very low level of steps. So any kind of data like this type of health data is something that we could populate in a histogram and possibly get a better sense of what's going on with that data set. Let's take a look at another one. This is going to be the distance. So how far we went. So again, we sorted it from lowest to highest. You've got a lot of zeros here. And then it's going up for the distance. And we've got a huge amount or pretty decent amount of data up to 27 up top. Okay. So we've got the distance. And if we graph that data set, and by the way, most of these data sets, if you want to practice with different data sets yourself, we're pulling from Kaggle. So you can say K-A-G-G-L-E K-A-G-G-L-E dot com. So here we have the distance in the buckets zero to 1.9, 1.9 to 3.8, 3.8 to 5.7. And the number of items, the count that falls into those buckets. Once again, we have it skewed to the right. So we have very few days that are way out here where we have this large distance. So let's take a look at another one. We have then the calories. All right. So now we've got the calorie data. It's kind of interesting. We've got something that's more centered looking. And so we've got, again, the dates on the left. We've sorted it by the number of calories. So you can see the calorie count going up as we go down the data set here. And if we take all that and we graph it, now we've got something that looks more like, you know, closer to a normal distribution, right? Now we've got something kind of in the middle. What you might expect with calories, right? Because you might expect that your normal intake, just in terms of your bodies, if you're gauging just on what your body's telling you to do, you're usually around a certain range, you would think, right? Because otherwise you would be gaining weight or losing weight over a longer period of time. You would think. So you've got then zero to 370, 370 to 740, 740 to 110 and so on. And then we have our midpoint here and it's tapering off to the left and to the right, which is something that you would kind of expect on a calorie distribution. Let's see what else we got here. And notice, like this kind of, like if I try to approximate a line with some of these, it would be difficult, maybe more difficult to try to plot like a line to give us some predictive power with these, right? If I have something, again, that looks more like a bell curve kind of thing, we'll talk about a bell curve distribution later. But notice that all the data that we have will not always fall into something that we can easily plot a line through it. We would like to have a function with it if we could, because then that gives us some predictive power, you know, with a mathematical function, which would be nice. So then we've got, then what do we have here? This is going to be, this is the, to be honest, I don't know exactly what the value is that's being represented. But this is an example that has negative values here. So note that we have a histogram that has, you know, negative values and then going into the positive. And we could still see it, you know, our center part over here and then it tailing off into the negative. Let's do another one here. So this is the GDP per capita current dollars. So we've got the GDP numbers. So now we're looking at economic data. And when you do that, you got to think per capita per person kind of thing, the GDP divided by the number of people. And so now we've got something that is skewed to the right again because we got the tail over to the right. So we took all of this data. And you can see we sorted it by, by the GDP per capita and from two to one to, to the 17, two to one. We have the largest amount here. And then, and then as we get the GDP per capita going up, we have many fewer that are falling into those buckets. So most are falling into the bucket on the lower buckets. And then as we have the GDP per capita going up, we have fewer falling into those buckets with an outlier way out here with the GDP per capita at 221, 221, which is interesting. You would think that would be a very well, well off place. So we can actually check it out down here. So if I scroll down, it's saying Monaco in this data set. All right, let's go back up top and see what the next one looks like. So now we've got activity per hour calories. So calories and we have a lot of the 42 and the calories going up on a per hour. So if we look at this kind of medical data, then we can compile tons of data, right, the stepping data, the calories per hour, the calories per day and whatnot. And then obviously we can, we may want to start to compile the data. So this one has a bunch of basically the outliers over here. So when we just simply plot this information, we've got it then skewed to the right. Now notice that these outliers are forcing us possibly to have these buckets, you know, maybe out here. So maybe it would be more useful for us to trim off some of these buckets and then we can kind of zoom in on more of the data that's in a relevant range. And so those are some techniques we could do with the graph or with Excel. And so let's see the next one. And so I have the name and the total. Oh, I think these are like Pokemon characters. There was another kind of, I thought it was a funny data set that was on the Kaggle website or Kaggle. I'm not sure how you say the website, but I think it was Pokemon characters. And I'm assuming this is like their power strength level, you know. So if we look at all of the characters and I'm not that familiar with Pokemon, but I think, you know, they fight each other like a card game. And then you have different power levels and who's going to win if the two were to fight each other or something like that. And there's different categories of the power and whatnot. So it gets kind of complicated. But if you just plot their power levels, you've got the 180 to the 241. And I'm assuming this is low power. So these are the weak ones, 241 to 302, 302 to 363, 363 to 424, 424 to 485. And then pretty high power level. Most of them seem to be in this fairly high power level, which is kind of interesting. 45 to 546. And then it drops out in sharply, sharply for the more powerful ones, 546 to 607. And then you've got the super powerful one over here, which apparently is, if I scroll all the way down, you've got the METTO, MOTO, MOTO, ME, EU2, METU, I don't know. I don't know who that is, but if you're a Pokemon, my takeaway from this data is you don't want to mess with that one, hopefully. But in any case, you can plot just about any set of data. That's the point. And you can get a pictorial representation of the idea of possibly what's going on. This might help you to determine how you play the Pokemon.