In statistics, simple linear regression is a technique we can use to quantify the relationship between a predictor variable and a response variable.

The following step-by-step example shows how to perform simple linear regression in Power BI.

**Step 1: Load the Data**

First, we will load the following table named **my_data** that contains information about total ad spend and total revenue generated by various retail stores:

**Step 2: Fit the Linear Regression Model**

Suppose that we would like to fit a simple linear regression model, using **Ad Spend** as the predictor variable and **Revenue** as the response variable:

**Response = β _{0} + β_{1}*(Ad Spend)**

To do so, click the **Table tools** tab and then click the **New table** icon:

Then type the following formula into the formula bar:

Model = LINEST( 'my_data'[Revenue], 'my_data'[Ad Spend] )

This will fit a simple linear regression model and create a table with various statistics that summarize the model:

**Note**: You can find the complete documentation for the **LINEST** function in DAX here.

The most important values in the output are the Slope1 and Intercept values, which we can use to write the fitted regression equation:

**Revenue = 8.67444 + 1.10958*(Ad Spend)**

Here is how to interpret the coefficients in the model:

- If a store spends zero dollars on ads then their predicted revenue is
**$8.67444**. - For each additional dollar that a store spends on ads, their predicted revenue increases by an average of
**$1.10958**.

**Step 3: Use the Regression Model to Make Predictions**

Next, we can insert the following scatter chart with **Ad Spend** on the x-axis and **Revenue** on the y-axis along with a trendline to visualize the relationship between the two variables:

Next, we will create a slider bar where we can change the value of **Ad Spend** and then see the predicted value for **Revenue**.

To do so, click the **Modeling** tab and then click the **New parameter** icon, then click **Numeric range** from the dropdown menu:

In the new window that appears, we’ll type **Ad Spend** as the Name, set the **Minimum** value to **0** and the **Maximum** to **20**, check the box next to **Add slicer to this page**, then click **OK**:

This will insert a new slicer that we can slide from 0 to 20:

Next, switch back to the Table view. Then click the **Table tools** tab and then click **New measure**:

Then type the following formula into the formular bar:

Predicted Revenue = SELECTCOLUMNS('Model', [Intercept]) + SELECTCOLUMNS('Model', [Slope1])*'Ad Spend'[Ad Spend Value]

This will create a new measure named **Predicted Revenue** that uses the slope and intercept of the regression model along with the value in the new slider bar to calculate the predicted revenue:

Switch back to the Report view and insert a Card visualization. Then use the new measure **Predicted Revenue** as the field for the card:

The card visualization displays the predicted revenue based on the value for **Ad Spend** in the slider.

For example, when **Ad Spend** is equal to **10**, **Revenue** is predicted to be **19.77**.

We can verify this is correct by manually plugging these values into the fitted regression equation we found earlier:

- Revenue = 8.67444 + 1.10958*(Ad Spend)
- Revenue = 8.67444 + 1.10958*(10)
- Revenue = 19.77

Feel free to move the value on the slider to see how various values for **Ad Spend** affect the **Predicted Revenue** value.

**Additional Resources**

The following tutorials explain how to perform other common tasks in Power BI:

How to Write a Case Statement in Power BI

How to Calculate Percent of Total in Power BI

How to Calculate Percent of Total by Category in Power BI