Labeling axes to fit data sets

0:00- [Instructor] So we have some data here. It says weekly average high temperature in Philadelphia. And what we wanna do is plot this on a coordinate grid. Now, to do that, let's get some graph paper going. All right, that'll make my grid a little bit more precise.

0:17And now, let's think about how I wanna plot it. So I'm going from Week 1 to Week 6. So my x-axis, this is the independent variable right over here, I just have to have these positive integers from 1 to 6. And then, my dependent variable, the temperature, it looks like it goes between 55 and 70 degrees.

0:36So I wanna make sure I have enough space for all of that. So let me do my vert, whoops. Let me do my vertical axis right over there. And then, let me do my horizontal axis like this. And so, what I'm going to plot on the vertical is temperature or high temperature, high temp.

1:02And then, on my horizontal axis, I'm going to plot what week it is and I have to go from one to six and let's see how many of these squares. If I went every three, I had to go one, two, three, four, five. Let me just go two, every two, because 6 doesn't fully fit.

1:21So if I go one, two, three, four, five, six. I could obviously keep going. And the reason why I was at least going for three or two boxes per hash here is I wanted to spread this out as much as possible. If I made it just one, two, three, four like that, it would have all been squanched up.

1:42And I want to be able to see my data and compare it nicely. Now, similarly on the vertical axis here, I have to get up, I have to get as high as 70 degrees. So if I went 10, 20, 30, 40, 50, 60, 70, that looks like it'll do the trick. So let me do this. It would be 10 degrees.

2:03Or maybe I'll just say this is all in degrees Fahrenheit. This is 20, this is 30, this is 40, this is 50, and this is 60, and that is 70. Notice, I had to look at my data to think about how I want to scale this actual coordinate grid. And this is all in degrees Fahrenheit right over here.

2:25And now, let's plot the points and I can color code it. So Week 1, 70 degrees. Week 1 is 70 degrees right over there. Week 2 is 60 degrees. Week 2 is 60 degrees right over there. Week 3 is 75 degrees. Oh, that was actually the high, but luckily I can actually still fit 75 here.

2:54I didn't see that when I first just scanned the data. That's an important thing to do. Week 3 is 75 degrees, so a little higher. Luckily, I can still fit that on my coordinate grid. Week 3 is 75. And then, we can go to Week 4 is 60 degrees. Week 4 is 60 degrees.

3:17We could go to Week 5 is 55 degrees. Week 5, 50, this is 50, this is 55 right over there. And then, last but not least, we have Week 6 at 70 degrees. Week 6 go all the way to 70 and we are done. So data looks nice. I can read. I can interpret it. It's not squanched up in either direction.

3:47I was able to fit all of the data on my grid and that's actually the big takeaway here. You want to be able to see your data and if I had done it, so it said 10, 20, 30, 40, I wouldn't even be able to graph most of my data. So that's why it's important to look at your data first.

4:03And then, think about the scale of your different axes so you can make sure you can fit it and look at it nicely and analyze it nicely.