The **method of least squares** is a method we can use to find the regression line that best fits a given dataset.

The following video provides a brief explanation of this method:

To use the method of least squares to fit a regression line in Excel, we can use the **=LINEST()** function.

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

**Step 1: Create the Dataset**

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

**Step 2: Use Method of Least Squares to Fit Regression Line**

We can use the **=LINEST(known_ys, known_xs)** function to use the method of least squares to fit a regression line to this dataset:

Once we press **ENTER**, the coefficients of the regression model will appear:

**Step 3: Interpret the Results**

Using the coefficients from the **=LINEST()** function, we can write the following fitted regression line:

y = 11.55211 + 1.07949(x)

We can use this equation to estimate the value of y based on the value of x.

For example, if x = 10 then we would estimate that y would be equal to **22.347**:

y = 11.55211 + 1.07949(10) = 22.347

**Step 4: Plot the Results**

Lastly, we can use the following steps to plot the dataset along with the fitted regression line:

- Highlight cells
**A2:B16**. - Click the
**Insert**tab along the top ribbon. Then click the first chart option titled**Insert Scatter (X, Y) or Bubble Chart**in the Charts group. - Once the chart appears, click the plus “+” sign in the top right corner. In the dropdown menu, click the checkbox next to
**Trendline**to add the fitted regression line to the chart.

**Additional Resources**

How to Perform Multiple Linear Regression in Excel

How to Perform Quadratic Regression in Excel

How to Perform Polynomial Regression in Excel

Curve Fitting in Excel (With Examples)