I have an, I think, rather basic question about linear regression to which I can not find a satisfactory answer.
I am trying to apply linear regression on a certain data set. First of all, I have detected outliers and high leverage values using studentized deleted residuals and leverage values. For this, I have just applied the most basic linear regression model on the data ($y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + ...$). I have done nothing with these outliers, didn't remove them from the data set.
After this, I have taken a closer look at the data and found out that I should transform the dependent variable y to ln(y). Furthermore, I have removed a couple of redundant variables to obtain a final model.
However, when I try to detect outliers and high leverage values using studentized deleted residuals and leverage values again, but this time with my final model, I obtain different outliers!
Now my question is: should you first detect outliers and then build a model, or the other way around?
Thanks very much for your help in advance!