The mean square error (MSE) is a metric that tells us how far apart our predicted values are from our observed values in a regression analysis, on average. It is calculated as:
MSE = Σ(Pi – Oi)2 / n
where:
- Σ is a fancy symbol that means “sum”
- Pi is the predicted value for the ith observation
- Oi is the observed value for the ith observation
- n is the sample size
To find the MSE for a regression, simply enter a list of observed values and predicted values in the two boxes below, then click the “Calculate” button:
Observed values:
Predicted values:
MSE = 2.43242