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In statistics, the **mean absolute error** (MAE) is a way to measure the accuracy of a given model. It is calculated as:

MAE = (1/n) * Σ|y_{i} – x_{i}|

where:

**Σ:**A Greek symbol that means “sum”**y**The observed value for the i_{i}:^{th}observation**x**The predicted value for the i_{i}:^{th}observation**n:**The total number of observations

The following step-by-step example shows how to calculate the mean absolute error in Excel.

**Step 1: Enter the Data**

First, let’s enter a list of observed and predicted values in two separate columns:

**Note:** Use this tutorial to if you need to learn how to use a regression model to calculate predicted values.

**Step 2: Calculate the Absolute Differences**

Next, we’ll use the following formula to calculate the absolute differences between the observed and predicted values:

**Step 3: Calculate MAE**

Next, we’ll use the following formula to calculate the mean absolute error:

The mean absolute error (MAE) turns out to be **2.5625**.

This tells us that the average absolute difference between the observed values and the predicted values is 2.5625.

In general, the lower the value for the MAE the better a model is able to fit a dataset. When comparing two different models, we can compare the MAE of each model to know which one offers a better fit to a dataset.

**Bonus:** Feel free to use this Mean Absolute Error Calculator to automatically calculate the MAE for a list of observed and predicted values.

**Additional Resources**

How to Calculate MAPE in Excel

How to Calculate SMAPE in Excel