One of the most common metrics used to measure the forecast accuracy of a model is **MSE**, which stands for **mean squared error**. It is calculated as:

**MSE **= (1/n) * Σ(actual – forecast)^{2}

where:

**Σ**– a fancy symbol that means “sum”**n**– sample size**actual**– the actual data value**forecast**– the forecasted data value

The lower the value for MSE, the better a model is able to forecast values accurately.

**How to Calculate MSE in Excel**

To calculate MSE in Excel, we can perform the following steps:

**Step 1: Enter the actual values and forecasted values in two separate columns.**

**Step 2: Calculate the squared error for each row.**

Recall that the squared error is calculated as: (actual – forecast)^{2}. We will use this formula to calculate the squared error for each row.

Column D displays the squared error and Column E shows the formula we used:

Repeat this formula for each row:

**Step 3: Calculate the mean squared error.**

Calculate MSE by simply finding the average of the values in column D:

The MSE of this model turns out to be **5.917**.

**Additional Resources**

Two other popular metrics used to assess model accuracy are **MAD** – mean absolute deviation, and **MAPE** – mean absolute percentage error. The following tutorials explain how to calculate these metrics in Excel:

How to Calculate Mean Absolute Deviation (MAD) in Excel

How to Calculate Mean Absolute Percentage Error (MAPE) in Excel

Very useful article, everything described in very convenient form, without mathematical/statistical hard language. I very appreciate it, thank You brother!