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.

The following step-by-step example explains how to create a residual plot for a simple linear regression model in Google Sheets.

**Step 1: Enter the Data**

First, let’s enter the following values for a dataset:

**Step 2: Calculate the Equation of the Regression Model**

Next, we’ll use the **SLOPE** and **INTERCEPT** functions to calculate the equation of the simple linear regression model for this dataset:

Using these values, we can write the following simple linear regression equation:

y = 29.631 + 0.755x

**Step 3: Calculate the Predicted Values**

Next, we can use the regression equation to calculate the predicted values for each observation.

We’ll type the following formula into cell **C2**:

=$B$16+$B$15*A2

We can then copy and paste this formula down to every remaining cell in column **C**:

**Step 4: Calculate the Residuals**

A residual is the difference between an observed value and a predicted value in a regression model.

It is calculated as:

**Residual = Observed value – Predicted value**

To calculate the residual for each observation in our dataset, we can type the following formula into cell **D2**:

=B2-C2

We can then copy and paste this formula down to every remaining cell in column **D**:

**Step 5: Create the Residual Plot**

To create the residual plot, we can highlight the values in the range **A2:A13**, then hold the “Ctrl” key and highlight the values in the range **D2:D13**.

Then click the **Insert** tab, then click **Chart** in the dropdown menu.

In the **Chart editor** panel that appears on the right side of the screen, choose **Scatter chart** as the Chart type:

The following residual plot will automatically appear:

The x-axis displays the values for the predictor variable in our regression model and the y-axis displays the residuals.

One key assumption of linear regression is that the residuals have constant variance at every level of x, so we often use a residual plot to determine if this assumption is met.

If the residuals are roughly evenly scattered around zero in the plot with no clear pattern, then we typically say the assumption of constant variance is met.

In our residual plot above we can see that the points in the plot seem to be randomly scattered around zero with no clear pattern, thus we would conclude that the constant variance assumption is met for this particular regression model.

**Additional Resources**

The following tutorials explain how to perform other common tasks in Google Sheets:

How to Perform Linear Regression in Google Sheets

How to Perform Polynomial Regression in Google Sheets