**Nonlinear regression** is a regression technique that is used when the relationship between a predictor variable and a response variable does not follow a linear pattern.

The following step-by-step example shows how to perform nonlinear regression in Excel.

**Step 1: Create the Data**

First, let’s create a dataset to work with:

**Step 2: Create a Scatterplot**

Next, let’s create a scatterplot to visualize the data.

First, highlight the cells in the range **A1:B21**. Next, click the **Insert** tab along the top ribbon, and then click the first plot option under **Scatter**:

The following scatterplot will appear:

**Step 3: Add a Trendline**

Next, click anywhere on the scatterplot. Then click the **+** sign in the top right corner. In the dropdown menu, click the arrow next to **Trendline** and then click **More Options**:

In the window that appears to the right, click the button next to **Polynomial**. Then check the boxes next to **Display Equation on chart** and **Display R-squared value on chart**.

This produces the following curve on the scatterplot:

Note that you may need to experiment with the value for the **Order** of the polynomial until you find the curve that best fits the data.

**Step 4: Write the Regression Equation**

From the plot we can see that the equation of the regression line is as follows:

y = -0.0048x^{4} + 0.2259x^{3} – 3.2132x^{2} + 15.613x – 6.2654

The R-squared tells us the percentage of the variation in the response variable that can be explained by the predictor variables.

The R-squared for this particular curve is **0.9651**. This means that 96.51% of the variation in the response variable can be explained by the predictor variables in the model.

**Additional Resources**

How to Perform Simple Linear Regression in Excel

How to Perform Multiple Linear Regression in Excel

How to Perform Logarithmic Regression in Excel

How to Perform Exponential Regression in Excel