The goal of a simple linear model is to fit a line onto this plot to summarize the shape of the data using the equation above.
The “a” value is the slope of the fitted line (rise over run) and the “b” value is the intercept on the y-axis (when x is equal to zero).
In the gapminder example, the life expectancy column was assigned as the “y” variable, as it is the outcome that we are interested in predicting or understanding. The year1950 column was assigned as the “x” variable, as it is what we are using to try and measure the change in life expectancy.
This is a little more complicated than adding a regression line to a scatterplot (the “normal” way to do linear regression with Power BI) but this method lets you work with the outputs in a way that the normal method doesn’t.