Scatterplots
Scatterplots are a very easy and powerful way to visualize the relationships between numeric variables (Measures). For example, I wonder if there is a relationship between Discount and Profit (seems like a reasonable possibility).
1. Let’s take a look by selecting those two fields and clicking Scatterplot on the Show Me menu.
Figure LW8.1: Creating our first scatterplot
2. Go to Analysis, uncheck Aggregate Measures.
3. Swap if needed to get Discount on the horizontal axis – looks like Profit tends to decrease as a larger Discount is given (confirms intuition).
Remember that with Tableau, unlike many other tools, we don’t need to stop here. It is very easy to
keep adding data.
4. Drag Segment to Color. This makes it easy to see what segment certain outliers belong to.
Figure LW8.2: Creating our first scatterplot, with color channel for segment.
Let us say that we want to focus on just one segment (while leaving all the data displayed).
5. Select Corporate segment by highlighting it from the Legend.
Figure LW8.3: Creating our first scatterplot, highlighting the Corporate segment.
6. Go back to viewing all the data by clicking anywhere on the worksheet.
7. Go to the Analytics pane and drag out a trend line (choose linear).
Figure LW8.4: Creating our first scatterplot, adding a linear trend line.
If you have had a statistics course, you probably realize that this is a regression line.
This trend line does not look very impressive but take a look at the y-axis scale. We need to investigate more before dismissing this as a negligible relationship. Hover over the trend line.
Figure LW8.5: Creating our first scatterplot, viewing the p-value associated with a trend line.
The p-value is very small (less than 0.0001) so relationship between these variables is statistically significant. And we can see by the negative coefficient (-251.388…) that the relationship is negative.
However, the R-Squared (0.0461243) value is somewhat low, so our simple trend line has not explained very much of the relationship. A little mini-review of correlation.
The quantity r, measures the strength and the direction of a linear relationship between two variables (Bennett, Briggs, & Triola). The formula for computing this value is very scary so let us be glad that Tableau (and stats programs, too) compute this value for us.
The value of r ranges from -1 < r < +1. The + sign are used for positive linear correlations and the – sign negative linear correlations (Bennett et al., 2018):
• Positive correlation: If x and y have a strong positive linear correlation, r is close to +1. An r value of exactly +1 indicates a perfect positive fit. Positive values indicate a relationship between x and y variables such that as values for x increases, values for y also increase.
• Negative correlation: If x and y have a strong negative linear correlation, r is close to -1. An r value of exactly -1 indicates a perfect negative fit. Negative values indicate a relationship between x and y such that as values for x increase, values for y decrease.
• A correlation greater than 0.8 is generally described as strong, whereas a correlation less than
0.5 is generally described as weak. These values can vary based upon the “type” of data being examined. A study utilizing scientific data may require a stronger correlation than a study using social science data.
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