You can use the **LOGEST **function in Excel to calculate the formula of an exponential curve that fits your data.

The equation of the curve will take on the following form:

y = b * m^{x}

This function uses the following basic syntax:

**=LOGEST(known_y's, [known_x's], [const], [stats])**

where:

**known_y’s**: An array of known y-values**known_x’s**: An array of known x-values**const**: Optional argument. If TRUE, the constant b is treated normally. If FALSE, the constant b is set to 1.**stats**: Optional argument. If TRUE, additional regression statistics are returned. If FALSE, additional regression statistics are not returned.

The following step-by-step example shows how to use this function in practice.

**Step 1: Enter the Data**

First, let’s enter the following dataset in Excel:

**Step 2: Visualize the Data**

Next, let’s create a quick scatter plot of x vs. y to verify that the data actually follow an exponential curve:

We can see that the data do indeed follow an exponential curve.

**Step 3: Use LOGEST to Find the Exponential Curve Formula**

Next, we can type the following formula into any cell to calculate the exponential curve formula:

=LOGEST(B2:B11, A2:A11)

The following screenshot shows how to use this formula in practice:

The first value in the output represents the value for **m** and the second value in the output represents the value for **b** in the equation:

**y = b * m ^{x}**

Thus, we would write this exponential curve formula as:

**y = 1.909483 * 1.489702 ^{x}**

We could then use this formula to predict the values of y based on the value of x.

For example, if x has a value of 8 then we would predict that y has a value of **46.31**:

y = 1.909483 * 1.489702^{8} = 46.31

**Step 4 (Optional): Display Additional Regression Statistics**

We can set the value for the **stats** argument in the **LOGEST** function equal to **TRUE** to display additional regression statistics for the fitted regression equation:

Here’s how to interpret each value in the output:

- The standard error for m is
**.02206**. - The standard error for b is
**.136879**. - The R
^{2}for the model is**.97608**. - The standard error for y is
**.200371**. - The F-statistic is
**326.4436**. - The degrees of freedom is
**8**. - The regression sum of squares is
**13.10617**. - The residual sum of squares is
**.321187**.

In general, the most interesting metric in these additional statistics is the R^{2} value, which represents the proportion of the variance in the response variable that can be explained the predictor variable.

The value for R^{2} can range from 0 to 1.

Since the R^{2} for this particular model is close to 1, it tells us that the predictor variable x does a good job of predicting the value of the response variable y.

**Related:** What is a Good R-squared Value?

**Additional Resources**

The following tutorials explain how to perform other common operations in Excel:

How to Use DEVSQ in Excel

How to Use SUMSQ in Excel

How to Perform Nonlinear Regression in Excel