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!
