 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 if you so choose, and either read or listen to the transcript in multiple different languages using the timestamps to tie into the actual video presentations. OneNote desktop version here continuing on with our theme, taking data, making pictorial representations of the data so we can get a different viewpoint, another angle at that data, hopefully being able to extract more meaning from it than we otherwise would be able to do. So we're going to be looking at more histograms now for a couple different reasons. One, we just want to think about how widespread this concept would be. 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 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 to 5,000, 5,000 to 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, a 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 that 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. So 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 0 to 1.9 1.9 to 3.8 3.8 to 5.7. And the number of items that count that falls into those buckets. Once again, we have it skewed to the right. We have a 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. And so 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 ordered 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, more closer to a normal distribution, right? Now we've got something kind of in the middle. And this is 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 is 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.