why would one choose to place the distributional assumption on the errors why do we put assumption on regression how to calculate residual distribution assumptions what is the difference between error and distribution why does regression residual assume assumptions why is it necessary to place the distributional assumption on the errors, i.e. $y_i who is responsible for the assumption of distributional distribution of the regression model? when making a distributional assumption, why would/should/should one choose to begin with assumptions on both data and the data? what is the distributional assumption of regression analysis? why is it necessary to place the distributional assumption on the errors why are residual distribution assumptions necessary in regression why is it necessary to place a distributional assumption on the errors a regression regression regression regression regression regression regression regression regression regression regression regression regression regression regression regression regression regression regression regression why is it necessary to place the distributional assumption on the errors i.e. x-beta x-epsilon-sim why is it necessary to place the distributional assumption on the errors? why is it necessary to place the distributional assumption on the errors? why is it necessary to place the distributional assumption on the errors in a regression model? why is it necessary to place the distributional assumption on the errors regression residual distribution assumption why is the distributional assumption placed on the errors? what is the difference between error and distributional assumption