A lot of what we'll be doing in this course is interpreting data. And one of the most important ways to do that is by taking that data and visualizing it with a chart or a graph of some sort. Now, unfortunately, there are many different types of graphs, and they all display numbers of observations, which is a word called frequency that's really important, versus different types of data. And all these charts and graphs have their own strengths and weaknesses. So, what I want to do in this video is give you a general overview as to the most important charts and graphs that you should know.
And then later on, we'll talk about each one of these in a little bit more detail. So let's go ahead and get started here. We're going to be talking about how we visualize different types of data. Alright? So I mentioned that there are two different types.
And basically, the first one is called qualitative or sometimes called categorical data. This is where the observations, the things that you're measuring, are names or labels and not numbers. So, for example, if I grab a group of people and ask what their eye colors are or their nationalities, I can't attach numbers to those things. Those are just categories or labels. So, let's talk about the two different types of charts that are most important and most common.
The first one is probably something you've seen before. It's called a bar chart. This is essentially where you're measuring the number of observations on the y-axis by these vertical bars, and then the categories are on the x-axis. And in the bar charts, the bars can be arranged somewhat randomly, but, essentially, the higher the bar, the higher the frequency. Alright?
If you ever hear something called a Pareto chart, all you have to know is that it's essentially a special type of bar chart in which the bars are now arranged not randomly, but in descending order. So here, it's basically just sorted. So it's the same data, except now you can see here more clearly what the two most important or more significant categories are, which are brown and hazel eyes. So that's what a bar chart and Pareto chart are. Let's move on to another one that, again, you're probably familiar with, you've seen before, which is called a pie chart.
So now, for example, let's grab a group of people and ask what their nationalities are, and I'll get four answers, USA, China, Canada, and India. And I want to know not how many observations or frequencies, but I want to know basically what percentage of the total those categories make up. That's what a pie chart is really helpful for. So pie charts show data as percents of a total. And basically, the bigger the wedge of the pie, the bigger the percent.
So both of these types of charts basically just use size to measure the amount of data, just in slightly different ways. Bar charts tend to use these things as numbers, where pie charts will always express these things as percentages or sometimes called proportions. Alright? So let's move on to the second type of data, which is not qualitative or categorical. It's called quantitative data.
This is where the observations, the things that you're measuring, are not names or labels, but they're actually numerical. So that's what quantitative data is. This is if I grab a group of students and I ask what their test scores are or I measure their heights. These are things that I can attach numbers to, like I got a 70 on my test or a 90 or I'm sixty-four inches tall or something like that. All right?
So the two most important types of graphs and charts are called histograms and frequency polygons and then stem plots. A histogram is essentially just a bar graph for quantitative data. So it's the same idea. You're going to measure frequency that's on the y-axis. The height of the bar measures the frequency, except now the classes on the x-axis are numbers instead of categories.
And, basically, because these things form a continuous set of numbers, like 50 goes into 60 and then 70 and so on and so forth, the bars will touch. Whereas in a bar chart, what happens here is that the bars usually are not touching. There's usually one way you can tell that you're working with a histogram. Alright? So, if you ever see something called a frequency polygon, I know that sounds kinda scary, but, essentially, all it is is it's just a line graph version of a histogram.
Essentially, these two charts show the exact same data just in slightly different ways. Instead of just drawing vertical bars, what they do is just they plot these points and then connect them with a line. It's just a way to save a little bit of ink. You don't have to sort of draw these bars and then shade them in, but it shows the exact same data. Alright.
Let's move to the last one. The last one is called a stem plot or sometimes called the stem and leaf plot. So here's the idea. You grab a group of numbers. And essentially, what you're going to do is you're going to take the leftmost digits, in this case, the tens place, and you're going to put these things on the left column.
And then you're going to take the right-most digits of each number, the ones places, and you're going to put them on the right column. And essentially, what this allows you to do is see which numbers are more common than others in the dataset, but you actually get to see with the real numbers themselves. So clearly, we can see here that the seventies are the most common numbers in this dataset. And what it allows you to do is basically sort of allows you to recreate each of the original numbers from the dataset, which is pretty cool. So it's essentially almost kind of like a sideways histogram, but you actually get to see the data values themselves.
Alright. So that's it for this one, folks. This is just a general overview. Let's go ahead and move on.
