Quickly calculate the Mean Squared Error (MSE) and Sum of Squared Error (SSE) for your statistical models. Our intuitive online tool helps you understand the accuracy of predictions and the total squared differences between observed and predicted values. Essential for data scientists and analysts.
Formula:
The Sum of Squared Errors (SSE) measures the total squared difference between observed and predicted values, indicating the residual variation. The formula is:
SSE = Σ (Yi - Ŷi)2
- Yi: Observed (actual) value
- Ŷi: Predicted value
- Σ: Summation across all data points
The Mean Squared Error (MSE) is the average of the squared errors, providing a measure of the average magnitude of the errors. It's calculated as:
MSE = SSE / n
- SSE: Sum of Squared Errors
- n: Number of data points