Is it worthwhile to use LASSO for variable selection even if it is cumbersome? My goal is to understand which variables out of 10 actually explain my dependent variable for a voter/election research.
Now I have put all 10 variables into a multiple regression and have filtered out all that have p value over 0.1. Now I have a model with 3 variables, all p values at 0.014 or far below and r=0.8.
It seems all fine.
Can't I just use this model? Or do I absolutely have to use lasso? Because I have tried to bring lasso to work for hours now. I can't do it (only experienced in excel). It's really frustrating all the errors R throws at me. If I have to use lasso, how can I do this the easiest? Is there any way to do LASSO or something similar with excel?

If its only the Feature Importance Aspect you are looking for , you can use RFE Method (Recursive Feature Elimination) as well.
But Lasso Regression takes care of Model not reaching Overfit or Underfit Situations as well as Feature Importance, by reducing all the Useless Features to Zero.
You can use Lasso Regression using GridSearchCV method, and then plot your Mean Train and Mean Test Errors accordingly, if you have different values of Regularization Parameters available. And for the best values of Errors, you can consider the value for the Regularization Parameter and fit your model with Train Data.
