When we have a set of data and we want to determine the relationship between the variables through regression analysis, we can create a curve that best fits our data points. Fortunately, Excel allows us to fit a curve and come up with an equation that represents the best fit curve.

*Figure 1. Final result: **Curve fitting*

**How to fit a curve**

In order to fit a curve to our data, we follow these steps:

- Select the data for our graph, B2:C17, which is a tabular result of the relationship between temperature and volume.

*Figure 2. Sample data for **curve fitting*

- Click
tab >*Insert*button >*Scatter*chart*Scatter*

*Figure 3. Scatter chart option*

- A Scatter plot will be created, and the
tab will appear in the toolbar under*Design*.*Chart Tools* - Click
>*Add Chart Element*>*Trendline**More Trendline Options*

*Figure 4. Add Trendline options*

- In the
window, select*Format Trendline*and set the*Polynomial*to*Order**“2”* - Check the option for “
”.*Display Equation on chart*

*Figure 5. Format Trendline dialog box*

Excel will instantly add the best fit curve for our data, and display the polynomial equation on the chart. We can add the R-squared value as a measure of how close our data points are to the regression line. We simply have to check the option for “** Display R-squared value on chart**”.

In curve fitting, we want the R-squared value to be as close to the value of 1 as possible. The image below shows our scatter plot with a polynomial trendline to the order of 2. The R-squared value is “**0.9759**”.

*Figure 6. Output: **How to fit a curve*

**Customize the best fit curve**

We can further customize our chart by adding axis titles, adjusting the minimum axis values and highlighting the best fit curve by changing the color.

*Figure 7. Output: Customize **best fit curve*

**Note: **

We can opt to add a linear trendline instead, but a linear curve fit is not the best fit for our data. As shown below, several data points fall far from the linear trendline.

*Figure 8. **Linear curve fit*