The mean squared error (MSE) is a metric that’s used to measure how far apart the predicted values and the observed values in a regression analysis.

The following calculator helps you find the MSE value. You only need to provide the observed (actual) and predicted values, then click the ‘Calculate’ button:

MSE = **9.50000**

The formula for calculating the MSE is as follows:

```
MSE = Σ(yi – ŷi)² / n
```

Here, `Σ`

is the sum of values `yi`

is the observed value, `ŷi`

is the predicted value, `n`

is the number of observations.

To calculate the MSE, you need to subtract `xᵗʰ`

predicted value from each `xᵗʰ`

observed value, then square the difference. Once you do that for all observations, sum the squared values and divide by the number of observations.

I hope this calculator helps. Happy analyzing!