How do you calculate the MSE in a linear regression model?

Mean Squared Error (MSE) measures the number of errors in statistical models. It assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE is equals to zero. As model error increases, its value increases. The Mean Squared Error is also known as the Mean Squared Deviation (MSD).

For example, in regression, the MSE represents the average squared residual

Image depicting the relationship between the residuals and the mean squared error.

As the data points fall closer to the regression line, the model has less error, in result decreasing the MSE. A model with less error produces more precise predictions

formula for MSE.