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The Mean Squared Error (MSE) is a commonly used statistical metric to measure the accuracy of a set of data points compared to a predicted value. It is often used in fields such as data analysis, machine learning, and statistics. In order to calculate the MSE in Excel, one can follow a simple formula which involves finding the difference between the actual values and the predicted values, squaring those differences, and then taking the average of the squared differences. This process can be easily implemented using the built-in functions in Excel, making it a convenient tool for calculating the MSE for various data sets. By using the MSE, one can gain valuable insights into the accuracy and variability of their data, allowing for better decision making and analysis.
Calculate Mean Squared Error (MSE) in Excel
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: