In this article, I will show how to calculate Mean Squared Error in Microsoft Excel with quick steps and clear illustrations.
In practical life, we may have to forecast data and when we come to know the actual data, we have the chance to know the accuracy of the forecast technique. MSE or Mean Squared Error is one of the measures we can use to do this.
What Is Mean Squared Error (MSE)?
First of all, let’s know the term, what is MSE?
“Mean Squared Error”, the name suggests what it means.
Mean Squared Error Formula:
MSE=(1/n) ✕ Σ(Actual Data – Forecast Data)2; where n is the number of occurrences.
So, Mean Squared Error is the mean of the square of differences between expected and actual values.
- MSE is always positive.
- The smaller the MSE, the better the accuracy of the forecast method.
Calculate Mean Squared Error in Excel: 3 Simple Ways
Before starting, let’s introduce the data which are going to be used in this article.
In this dataset, we have some actual and budget amounts for several months of a year. The budget amounts are predicted using a forecasting technique and now, we will proceed to find the accuracy of this forecast model using Mean Squared Error measurement.
So, let’s see the methods to do this, one by one.
1. Calculation of Mean Squared Error Using SUM and COUNTA Functions
In the first method, we will use the SUM and COUNTA functions to find the Mean Squared Error. The steps are simple.
🔀 Steps:
- Add a new column named Difference on the right side and insert the following formula in the first cell of this column (i.e. cell E5). After that, drag the fill handle to copy this formula to the rest of the cells.
=C5-D5
- After that, add another column which is named Difference2, and insert the following formula in cell F5.
=E5^2
- Similarly, copy this formula to all cells in this column.
- Now, create a part just below the data, where the Mean Squared Error calculation will be performed.
- In cell F18, insert the following formula.
=SUM(F5:F16)
- This will give you the ∑(Actual ~ Forecast)2 value.
- After that, write the following formula in cell F19.
=COUNTA(B5:B16)
- This will give you the number of months.
- Finally, divide the value in cell F18 by the value in cell F19 using the following formula.
=F18/F19
- Hence, the Mean Squared Error turns out to be 6.667.
Read More: How to Calculate Root Mean Square Error in Excel
2. Calculation of Mean Squared Error Using AVERAGE Function
Here is another way to find Mean Squared Error, easier than the first one. In this method, we will utilize the AVERAGE function of Excel.
🔀 Steps:
- Just like in method 1, add two columns to calculate differences and squared differences.
- After that, insert the formula below to get the Mean Squared Error.
=AVERAGE(F5:F16)
Here, F5:F16 is the range of cells that has the square of differences between actual and predicted values.
Read More: How to Remove #DIV/0! Error in Excel
3. Calculation of MSE Using SUMSQ and COUNT Functions
In the last method, we will use the SUMSQ and COUNT functions to calculate the Mean Squared Error.
🔀 Steps:
- Just as in method 1, this time add one column on the right side to calculate the differences between the actual and predicted values.
- After that, use the following formula.
=SUMSQ(E5:E16)/COUNT(E5:E16)
Here, E5:E16 is the range of cells that has the differences between actual and predicted values.
Quick Notes
To find the Root Mean Square Error in Excel, just merge the SQRT function with the last output you get in any of the sections stated above. For example, you can use the following formula in the case of method 3.
=SQRT(SUMSQ(E5:E16)/COUNT(E5:E16))
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Conclusion
So, we are here to conclude now. Hope you have enjoyed this article. If you still face any problems in calculating Mean Squared Error in Excel.
