# You Can Now Run Shapley Regression in Displayr

Shapley Regression, also known as Shapley Value Regression, is the leading method for driver analysis. It calculates the importance of different predictors in explaining an outcome variable and is prized for its ability to address multicollinearity. You can now use Shapley Regression in Displayr.

## How to compute Shapley Regression in Displayr

- Go to
**Insert > Regression > Linear Regression.** - Select the
**Outcome**and**Predictor(s)**. These should be coded:- As numeric (e.g.
**Numeric**or**Numeric Multi**in**Structure**) - So that higher levels of performance/satisfaction have higher numbers (this isn't a technical requirement, but it makes interpretation a lot easier).

- As numeric (e.g.
- Change
**Output**to**Shapley Regression**.

## Interpreting the output

The output below shows a Shapley Regression of cell phone providers. The first column shows the estimated **Importance **of the drivers. We can see that Network Coverage is the most important. The absolute values of these importance scores add to 100.

Note that we have a negative value for 'Cancel your subscription/plan'. This is a special feature of our Shapley Regression. In the background, we also run a traditional linear regression and use its signs in the Shapley, as a way of alerting the user to the possibility that some of the effects may be negative. You can turn this feature off by selecting the option **Absolute importance scores**.

The second column shows the **Raw score**, which is the same as **Importance, **except that rather than adding up to 100, it adds up to the R-squared statistic, which in this case is 0.3871 (shown in the footer). Thus, we can say that Network coverage, for example, explains 7.3% of the variance in Net Promoter Score (the outcome variable).

## Johnson's Relative Weight

While Shapley Regression is very popular, my personal preference is to use Johnson's Relative Weights, which give near-identical results to Shapley Regression, but it can also be applied with categorical outcome variables. This method is available by setting **Output **to **Relative Importance Analysis**.

## Learning more about Shapley Regression

This post describes the basic math of Shapley Regression.