The bulk of this post is dedicated to how you can use scatterplots for explanatory purposes despite them being a more technical graph type. We’ll look at a few common multi-dimensional variations in a moment, but before we do, let’s break down how to read a scatterplot. In its standard form, as seen above, scatterplots show the relationship between two things, but it’s not uncommon to display more than two dimensions, especially when exploring your data. Scatterplots are very similar to line charts in that they both display two numerical values however, scatterplots tend to focus on individual data points (depicted with a dot) rather than aggregating multiple points into one distinct line. It’s not wrong to invert these, but it might be unexpected causing an initial bit of confusion. It’s common practice to place the independent metric along the horizontal or x-axis and the dependent variable along the y-axis. ![]() A dependent variable is likely the thing you are trying to measure, meaning it is affected by your independent variable. An independent variable is exactly what its name implies: it’s not affected by the other variable. Sometimes you’ll have both an independent and a dependent variable. In this example, it doesn’t matter which variable is along the horizontal or vertical axis, but that won’t always be the case. It all depends on your audience and specific scenario.īe mindful of the variable placement on the axes. I’ve categorized and labeled the points to make this graph legible, but an alternative chart-one that is more familiar-could work as well ( check out this post to see three alternatives for comparing metrics). I can see both sets of rankings simultaneously and also emphasize the hole in the market. Although the graph used in the discovery phase is not always ideal for communicating final insights, it works in this case. ![]() Many statistical software packages output scatterplots to test the correlation between two variables. A scatterplot was likely used to uncover this finding. Here are a couple of things worth noting about the above chart. You might consider showing the relationship between male and female rating scores using a scatterplot, like the one below. You uncover that lip care products are polarizing between male and female buyers, so there is an opportunity to create a new product that bridges the gap. ![]() Before embarking on this endeavor, you are asked to do some research to see if there is a hole in the market. Imagine you’re an analyst in the beauty industry and your company wants to formulate a new lip care product. Let’s look at a scenario where a scatterplot works nicely to communicate a finding. This is not to say you should never communicate with one, but you should take explicit steps to make sure your chart is clear to an unfamiliar audience (something you should do with all charts!). They are common in scientific fields and often used to understand data rather than to communicate with it. ![]() Let’s explore some of the basics of scatterplots via an example I’ll also cover tips for designing more effective ones and discuss common variations (bubble charts, connected scatterplots, etc.), too!Ī scatterplot shows the relationship between two numerical variables plotted simultaneously along both the horizontal and vertical axis. Regardless of your current comfort level, scatterplots are extremely useful to focus on the relationship between two series-a scenario that is common in both technical and non-technical fields. However, if you don’t perform a lot of statistical analysis, then these charts may be unfamiliar. A scatterplot is a niche chart, but it’s one of my favorites! If you are a statistician or work in a technical field, a scatterplot might be your go-to graph type.
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