Graphing Marathon Measures #3 – Scatter Diagram
One strategy I’ve been using lately to be less of a scatter-brain is to go for as many runs as possible. I just ran in my second half-marathon this past weekend and it was an amazing time to think and focus. Sometimes I come up with my most important ideas while I’m running around my neighborhood with my music blaring. In this article I’ll use some of my marathon training data to explain a very useful chart called a scatter diagram.
Half Marathon Finish
Before we draw some graphs, let’s set some general ground rules for chart creation.
Rule 1: Make sure you have a clear purpose for your graph and that it will convey an important message.
Rule 2: Try to use simple pictures to depict complex data.
Rule 3: Try to make your data talk and tell interesting stories.
Rule 4: Remember to adapt your graph to suit the audience.
Rule 5: Don’t be afraid to experiment with various options and graph styles.
Figure 1: 100% relationship
Figure 2: More common scatter
To draw a scatter diagram you need at least 20 “paired variables” which basically means you need 3 pieces of information about the 20 dots on the chart. You need the first variable like weight in figure 2, a second variable like body fat in figure 2, and something that pairs the two variables together like the date in figure 2. Each single dot on the chart represents a point in time for both weight and body fat. If I was hand drawing this chart I ask myself what was my weight on September 30, and what was my body fat on September 30. My weight on that day was 210 pounds and my body fat was 24%, I look at the Y axis (the one on the left) and find 210 pounds then I look at the X axis and find 24% and draw a dot in the spot that lines up with those 2 measures. I use this exact method at least 20 times for all the data and I end up with my scatter diagram.
You use a scatter diagram whenever you need to study and identify the possible relationship between two different sets of variables. In figure 3 we are looking at the relationship between weight loss and number of km’s run in the previous month.
It’s important that I point out that I’m not saying that any of these relationships are “cause and effect” relationships. Looking at scatter diagrams alone we can never make this claim. To understand cause and effect relationships more work and tools are required. Scatter diagrams show relationship, not cause.
At the end of the day you want the charts to tell stories. For example looking at the three charts we can make the following fact based statements.
Figure 1: There is a very strong positive relationship between my BMI and my weight. As my weight goes up, so does my BMI.
Figure 2: There is a relationship between my weight and my body fat. Generally as my weight goes up my body fat also goes up.
Figure 3: There seems to be an inverse relationship between the number of KM’s I run and the number of pounds I lose. If I run more km’s I seem to lose more weight in the next month.
Common uses for scatter diagrams:
- Relationship between customer satisfaction and employee satisfaction
- Relationship between turnaround time and volume of work
- Relationship between employee engagement and commitment to quality
- Relationship between anything and anything…
Adam Stoehr, MBA
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