A **residual plot** is a type of plot that displays the fitted values against the residual values for a regression model.

This type of plot is often used to assess whether or not a linear regression model is appropriate for a given dataset and to check for heteroscedasticity of residuals.

This tutorial explains how to create a residual plot for a simple linear regression model in Excel.

**How to Create a Residual Plot in Excel**

Use the following steps to create a residual plot in Excel:

**Step 1: Enter the data values in the first two columns. **For example, enter the values for the predictor variable in A2:A13 and the values for the response variable in B2:B13.

**Step 2: Create a scatterplot. **Highlight the values in cells A2:B13. Then, navigate to the *INSERT *tab along the top ribbon. Click on the first option for *Scatter *within the *Charts *area.

The following chart will appear:

**Step 3: Display ****trend line**** equation on the scatterplot. **Click “Add Chart Elements” from the *DESIGN *tab, then “Trendline”, and then “More Trendline Option. Leave “Linear” selected and check “Display Equation on Chart.” Close the “Format Trendline” panel.

The trend line equation will now be displayed on the scatterplot:

**Step 4: Calculate the predicted values. **Enter the trendline equation in cell C2, replacing “x” with “A1” like so:

Then, click cell C2 and double-click the small “Fill Handle” at the bottom right of the cell. This will copy the formula in cell C2 to the rest of the cells in the column:

**Step 5: Calculate the residuals.** Enter *B2-C2* in cell D2. Then, click cell D2 and double-click the small “Fill Handle” at the bottom right of the cell. This will copy the formula in cell D2 to the rest of the cells in the column:

**Step 6: Create the residual plot. **Highlight cells A2:A13. Hold the “Ctrl” key and highlight cells D2:D13. Then, navigate to the *INSERT *tab along the top ribbon. Click on the first option for *Scatter *within the *Charts *area. The following chart will appear:

This is the residual plot. The x-axis displays the fitted values and the y-axis displays the residuals.

Feel free to modify the title, axes, and gridlines to make the plot look more visually appealing:

This was very helpful. It would be even more helpful if it offered some guidance on how to evaluate the residuals other than visually and, more importantly, how to correct for any bias.

Thanks for this.

This is of great use, thank you for your help!

I just want to say, you did such a great job explaining this to someone who has no idea what they are doing. I feel really confident in the work I am going to submit. You are awesome. Keep it up for us less fortunate. 🙂

THANKYOU SO MUCH I LITERALLY WAS GOING TO FAIL MY ASSESMENT

Thanks for this Zach, it really helped. One error I’m noticing in the blog post, for the residual plot, you used Predictor vs Residual, but it should be Predicted vs Residual.

Super helpful, thank you so much. Easy and clear instructions.

This was really helpful. Thank you.

Kindly add the interpretation next time or update it.