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So far in this course, we've cared a lot about frequencies, frequencies across categories, frequencies across classes. But what if we care more about how data is changing over time as opposed to how it's changing across groups? So far, we haven't had a great way to display that type of data, but that's where the time series graph comes in. Time series graphs are great options for displaying how a variable's value changes over time, and I think you'll see that in this video. The data on a time series graph are coordinate pairs, so that means we have an x and a y.

To plot that type of point, we head to a grid. We go across x, up y, plot where we land, and then we're all set. For time series graphs, x will give us the time and y will give us the value measured at that time. Once we have all of our points plotted, we're going to connect them with segments, much like we see over here. The reason we do that is so that we can clearly see trends in our data, and some of the trends we care most about are increases and decreases in our dataset.

As we can see over here, it looks like our data's decreasing because we are heading down as we go. Alright. Let's take a peek at our example and create our own time series graph. In our example, we're given data about the average daily ice cream sales each month over the course of a year. And when we see over the course of time interval, then we know that we're measuring data over time, which means a time series graph is a great option for displaying that data.

We are in fact gonna create a time series graph, and we're gonna use it to look for trends. So we're gonna see, hey. When are sales increasing and when are they decreasing? Cool. Now I can see over here that x is in fact time. It's it's months, and I can see that the month number is increasing. So I am measuring data over a span of time. I wanna make sure that my x axis is labeled for months and that I choose a scale that allows me to see the entire range of time over which I measure data. So I'm gonna start with month one and just increment by ones here so, again, I'll be able to see the entire span over which I measured data. Perfect.

My y gives me the sales, which is what I measured at each month. Now my y axis is already all set to show me the sales, so I'm ready to start plotting points. It looks like in month one, the average daily ice cream sales were three. So that gives me the point 1,3. To plot that point, I'm gonna go across one and up three and then just plot where I land.

I'm gonna do that for each of my months. So it looks like in month two, average sales were two cones per day. So I'm going to plot the point 2,2. Looks like my next point is 3,4 followed by 4,6, 5,8, and 6,14. As you can see, the rest of my points have already been added to the graph, but feel free to verify that they're in the right place by checking with the table.

Perfect. Now I'm just gonna connect those points using segments so that I'll be able to clearly see trends in my data. And, again, we wanna look for periods of increase or decrease. For periods of increase, I should be heading up, and for periods of decrease, I should be heading down. Looking at my graph, I can see that I'm heading up from month two to month seven.

So that's gonna give me a period of increasing. But it doesn't look like sales are increasing at any other place in my graph. For decreasing, I should be heading down. And I can see here it looks like I'm heading down from month one to month two, and it also looks like I'm heading down from month seven to month twelve. So it looks like I have another period of decrease from month seven to month twelve.

Awesome work. Now that we've created our time series graph and analyzed it for trends, I think you're ready to head over to the practice. Best of luck.

Welcome to our time series graph example. Now right away, you might have noticed that the graph looks a little bit different because we have two different sets of points plotted. We have our blue set, and we have our orange set. Now it's actually totally fine to graph two different sets of data in the same grid as long as that data was collected over the same period of time. And in fact, that can help us to quickly visually compare two different datasets and how they changed over time in a time series graph.

Let's take a peek at our example. So in this problem, we're given data on the number of rabbits in two different pet stores over the course of eleven months. Again, we see over the course of we know data is measured over time. This is a time series graph. We wanna use the data to answer questions a, b, and c.

For question a, we're asked to find how many rabbits each pet store has in month three. I know that my x-axis gives the months, so I'm gonna start by locating month three. And then I'm just gonna look at each of my two datasets. I'm gonna start with my dataset in blue for store A. And I noticed that when x=3 or when months is 3, the y-coordinate is 3.

That means that store A has 3 rabbits in month 3. I'm gonna do the same thing for my orange set or my data on Store B. I noticed that when my months are 3, that my y-coordinate is 6. So I noticed that Store B has 6 rabbits in month 3. Now let's hop to question b.

I'm looking for the maximum amount of rabbits in each pet store, and I wanna figure out which month that maximum occurs in. Well, the maximum number of rabbits will be the point with the highest y-value. So I'm gonna start by locating my peak in my blue set of data. That peak occurs right here, and I notice that the y-coordinate of that peak is 8. So I know that my maximum number of rabbits is 8.

It has an x-coordinate of 6, meaning that this occurs in month 6. For Store B, I'm just gonna do the same thing. I'm gonna locate the point with the highest y-coordinate, which in this case is that point right there. And I noticed it has a y-coordinate of 9, meaning the maximum number of rabbits is 9. I also noticed that the x-coordinate is 11, meaning that this occurs in month 11.

Great. Now let's take a peek at our final question. Our final question asks us to locate general trends for each of our pet stores. As we talked about in the concept video, a great general trend to focus on is when we're increasing or when we're decreasing. So let's identify periods of increase and decrease for each pet store.

I'm gonna start with my first pet store, Pet Store A. When my data is increasing, I should see my lines generally heading up. And I noticed that happened one time for Store A right here from months 2 to month 6. Decreasing is when my data is heading down, and I noticed that happened three times for Store A. It happens right here.

It also happens right here, and it happens right here. Notice that I didn't ever circle from months 9 to 10 because I actually stayed level. I had the same number of rabbits from month 9 to month 10. So that's neither an increase nor decrease. Alright.

So it looks like my decreases occurred from months 1 to 2, from months 6 to 9, and months 10 to 11. Now let's take a peek at Store B, my orange set of data, and I'm gonna do the exact same thing. For Store B, I am increasing when my data is heading up. Now I notice an increase right here. I notice an increase right here, right here, and right here because these are the places where my data is heading up.

My first increase happens from month 1 to month 3, then it looks like it happens from month 4 to month 5. My next period is month 6 to month 7. And after that, I have from month 10 to month 11. Now let's check for decreases or when my data is heading down. I can see that it's heading down here.

I can also see that it's heading down here, and it's heading down here as well. So I can see a decrease from month 3 to 4. I can also see a decrease from month 7 to 8, and I can also see a decrease from month 9 to 10. Again, I didn't count month 5 to month 6 because my value stayed the same, so it was neither an increase nor a decrease. Awesome work.