The Classical model makes two roughly equivalent assumptions to ensure the indep
ID: 3205701 • Letter: T
Question
The Classical model makes two roughly equivalent assumptions to ensure the independence of the explanatory' variables from the equation's randomly distributed residuals: either the explanatory variables are assumed fixed in repeated resampling, or the explanatory variables change in each sample but do so randomly. Why are these assumptions equivalent, i.e. why would either one of these assumptions ensure the independence of the explanatory variables from the (randomly distributed) residuals? Explain.Explanation / Answer
In a classical model, repeated resampling makes the model very robust and ensures that no correlated variables are coming up in the model. In case of some advanced machine learning techniques, some variables are chosen along with other variables which are having weak correlation, but these are non linear techniques, but whereas in the case of a classical model we have repeated resampling which ensures the independancy of explanatory variables
Random sampling of explantory variables in each sample explains that there are different types of explanatory variables coming up and having random sample each time ensures the independence of the explanatory variables