how to calculate simple linear regression equation

You are not limited to using measures for known X and Y values - you can also use columns, thanks to row context being present in SELECTCOLUMNS. Note how we show known weight values when weight is known, but we display estimated weight in case where weight is not known.

We can now estimate Dumbledore’s weight and, with a little more DAX, build the following graph:

Blue part with your dependent values measure.

Green parts with your independent values measure (twice).

Red part with your category values column reference.

Simple linear regression can be done in just one measure: Simple linear regression = Linear regression has been done in DAX before (by Rob Collie and Greg Deckler, for instance), but my approach uses the new DAX syntax, which makes the calculations very easy. Note how Dumbledore’s weight is unknown - we are going to predict it with simple linear regression. Let’s say you have the following data model: Name With simple linear regression, you can estimate the quantitative relationship between any two variables. Update 2 December 2017: the sales example was updated to display the correct Estimated Sales figure at the grand total level. But first, why would you want to do such analysis? The two functions can be used for a simple linear regression analysis, and in this article I am sharing patterns to easily replicate them in DAX. As of 2017, some of the functions, such as SLOPE and INTERCEPT, exist in the latter but not in the former.

DAX, originating in Power Pivot, shares many functions with Excel.

How to calculate simple linear regression equation