Let’s take some example information and show how it could be used in a line graph. We can use the weather as an example. Below, we have information that shows the average temperature from two different locations: Anchorage, Alaska and Melbourne, Australia.

This is useful information but it can be tricky to see how the temperature changes over time. If we put this information into a line graph, it could look like this:

Showing the information from a chart allows us to more easily see patterns. For example, the line graph shows that in the beginning of the year, the average temperature starts colder, then increases in the summer, and cools off again near the end of the year. We can also see that the average temperature in Melbourne, Australia shows the opposite pattern: in January, the weather is hotter, then cools off in the middle of the year, and then warms up again at the end of the year.

What other patterns do you observe?

• Is Melbourne ever colder than Anchorage?
• When are the temperatures most similar between these two locations?

It’s important to note that axes can change dramatically and have different scales. Our weather example used months as the x-axis. However, we could look at different scales, or time intervals, such as:

• How the temperature changes every hour
• How the temperature changes every day
• How the temperature changes every week

How can line graphs be used to study epilepsy?

Now that we’ve walked through a common example, let’s look at how this could be used in a scientific paper, in which an animal model is used to study epilepsy and determine if a treatment is effective.

So in this example, a scientist is using a new mouse model to study epilepsy. First, the scientist needs to show that the mouse model shows a specific and measurable characteristic. One way to measure this is to count the number of seizures the sick mouse has each day. One way a scientist might do this is to first measure the number of seizures in a healthy mouse compared to a sick mouse with epilepsy. An example graph may look like this:

Here, the blue line shows the average number of seizures the healthy mouse has had per day since birth. Most of the time, a healthy mouse does not have seizures but occasionally they do. The orange line shows that the sick mouse has had multiple seizures per day since birth.

What are some of the other conclusions from this graph? Here are a couple:

• The sick mouse’s seizures started shortly after birth
• The number of seizures the sick mouse had per day dropped 5 days after birth and then remained fairly stable

Since the sick mouse is a new model of epilepsy, this shows important data. This data suggests that the sick mouse experiences seizures and can be a useful model to test new treatments.

Next, a scientist may want to determine whether a treatment can help reduce the number of seizures in this epilepsy mouse model. They may give a sick mouse a new treatment and measure the number of seizures. This type of graph can show how that seizure data compares with the same measurements taken from a healthy mouse and from a sick mouse that hasn’t received the new treatment.

This graph shows:

• Treatment 1 slightly decreases the number of daily seizures

However, let’s say that the scientist tried 2 new treatments, Treatment 1 and Treatment 2. A graph used to show that may look like this:

This graph is starting to get a little crowded but is showing important information. This graph shows:

• The sick mice have multiple seizures per day
• Healthy mice have very few seizures
• Treatment 1 slightly reduces the daily number of seizures
• Treatment 2 greatly reduces the daily number of seizures
• Neither treatment completely eliminates seizures

This type of information is extremely valuable for scientific research and shows how a line graph can be used to present important findings from studying epilepsy. There are many other types of information that can be shown in a line graph but now that we know about one of the main purposes of a line graph – to show change over time – hopefully you will be able to understand how to interpret the next line graph you see!