In this article, you will learn-

## What is Root Mean Square Error (RMSE)?

**Root Mean Square Error (RMSE)** quantifies how much mistake there is between two data sets. in other words, it compresses a predicted value and an observed or known value. The littler and RMSE esteem, the closer anticipated and watched values are.

It’s otherwise called Root Mean Square Deviation and is one of the most generally used statistics in GIS.

It’s also known as Root Mean Square Deviation and is one of the most widely used statistics in GIS.

Different than Mean Absolute Error (MAE), we use RMSE in a variety of applications when comparing two data sets.

Here’s a case of how to ascertain RMSE in Excel with 10 watched and anticipated qualities. Be that as it may, you can apply this equivalent calculation to any size data set.

## Root Mean Square Error Example

For example, we can compare any predicted value with an actual measurement (observed value).

Predicted value

Observed value

Root mean square error takes the distinction for each watched and anticipated worth.

You can trade the order for subtraction because the next stage is to take the square of the distinction. This is on the grounds that the square of a negative worth will consistently be a positive value. But just make sure that you keep the same order throughout.

After that, divide the sum of all values by the number of observations. Finally, we get an RMSE value. Here’s what the

** RMSE Formula** looks like:

## The most effective method to Calculate RMSE in Excel

Here is a quick and easy guide to calculate RMSE in Excel. You will need a set of observed and predicted values:

## 1 Enter headers

In cell A1, type “observed value” as a header. For cell B1, type “predicted value”. In C2, type “difference”.

## 2 Place values in columns

If you have 10 observations, place observed elevation values in A2 to A11. In addition, populate predicted values in cells B2 to B11 of the spreadsheet

## 3 Find the difference between observed and predicted values

In column C2, subtract observed value and predicted value. Repeat for all rows below where predicted and observed values exist.

=A2-B2

Now, these values could be positive or negative.

## 4 Calculate the root mean square error value

In cell D2, use the following formula to calculate RMSE:

=SQRT(SUMSQ(C2:C11)/COUNTA(C2:C11))

Cell D2 is the root mean square error value. And save your work because you’re finished.

If you have a smaller value, this means that predicted values are close to observed values. And vice versa.