You can use the **LINEST **function in Excel to fit a multiple linear regression model to a dataset.

This function uses the following basic syntax:

**=LINEST(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: Use LINEST to Fit Multiple Linear Regression Model**

Suppose we would like to fit a multiple linear regression model using **x1**, **x2**, and **x3** as predictor variables and **y** as the response variable.

To do so, we can type the following formula into any cell to fit this multiple linear regression model

=LINEST(D2:D14, A2:C14)

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

Here’s how to interpret the output:

- The coefficient for the intercept is
**28.5986**. - The coefficient for x1 is
**0.34271**. - The coefficient for x2 is
**-3.00393**. - The coefficient for x3 is
**0.849687**.

Using these coefficients, we can write the fitted regression equation as:

**y = 28.5986 + 0.34271(x1) – 3.00393(x2) + 0.849687(x3)**

**Step 3 (Optional): Display Additional Regression Statistics**

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

The fitted regression equation is still the same:

**y = 28.5986 + 0.34271(x1) – 3.00393(x2) + 0.849687(x3)**

Here’s how to interpret the other values in the output:

- The standard error for x3 is
**0.453295**. - The standard error for x2 is
**1.626423**. - The standard error for x1 is
**1.327566**. - The standard error for the intercept is
**13.20088**. - The R
^{2}for the model is**.838007**. - The residual standard error for y is
**3.707539**. - The overall F-statistic is
**15.51925**. - The degrees of freedom is
**9**. - The regression sum of squares is
**639.9797**. - The residual sum of squares is
**123.7126**.

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** .838**, it tells us that the predictor variables do 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 LOGEST Function in Excel

How to Perform Nonlinear Regression in Excel

How to Perform Cubic Regression in Excel