we can use the sum of squared errors and the mean squared error to assess the fit of a model. The mean squared error is also known as the variance. As such, the variance is a special case of a more general principle that we can apply to more complex models, which is that the fit of the model can be assessed with either the sum of squared errors or the mean squared error. Both measures give us an idea of how well a model fits the data: large values relative to the model indicate a lack of fit.
