Because their goal is reliable inference, many of the methods of classical statistics revolve around identifying and eliminating sources of bias.So, for a classical statistician, model validation is much more involved than applying CV and selecting the model with the maximum accuracy/minimum error.The analysis of residuals plays an important role in validating the regression model.If the error term in the regression model satisfies the four assumptions noted earlier, then the model is considered valid.For example, working with a regression model, the classical statistician wants reliable estimates of the true values of the regression coefficients.
I have been wondering ever since about the validation techniques that hard-core statisticians consider and/or use as model validation techniques.
I am assuming that it would not just be hypothesis testing and p-values.
Is there something fundamental I am missing about how core-statisticians work during the model validation process?
(Algebra 1 - Additional Cluster) (Algebra 2 - Additional Cluster) - Clusters should not be sorted from Major to Supporting and then taught in that order.
To do so would strip the coherence of the mathematical ideas and miss the opportunity to enhance the major work of the grade with the supporting clusters.
If the residual analysis does not indicate that the model assumptions are satisfied, it often suggests ways in which the model can be modified to obtain better results.