1/25/2024 0 Comments Analyzing scatter plots![]() ![]() We could also set this up to find any outlier that we might want to showcase for a particular filter and context.įor example, if we want to drill into our worst performers, we simply adjust the values in our axles. Once we get this grid, we can click through time to see who’s where. To do this, we’ll put some values in the X-Axis Constant Line and the Y-Axis Constant Line. When we have quite a lot of information in the scatter plot, we want to break it up into four grids. What I really like in the analytics function is that we can put a constant line in our chart so we can create a grid. Trend Line makes it easy for us to see who’s up and who’s below the trend. It may depend on what context you have filtered in your results, but all these native analytics functions will always change for the context as well. The analytics section is just on the right side corner of the Power BI Desktop and we can see heaps of options. ![]() But now with this built-in analytics in Power BI, highlighting good versus bad results in our visuals is so much easier. ![]() I’ve shown a number of examples where we could detect outliers, demonstrating who’s good and who’s bad. What’s cool about this and Power BI, in general, is its ability to quickly change the selection, so we can jump to any quarter and see our outliers. They have large sales and a big difference from last year. So Elizabeth and Charlotte are doing incredibly well. The Difference in Sales is below zero, as we can see in the chart. On the chart, we can see our good and poor performers.Īlong the X axis we’re seeing our good performers on the upper right-hand side, and our poor performers on the left-hand side. I’ve run some simple calculations here, comparing Total Sales with the Difference in Sales between this year and last year. ![]()
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