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?


My goal is to understand which variables out of 10 actually explain my dependent variable for a voter/election research.

This is not variable selection!
With variable selection the goal is to reduce the size of a model

*

*Such that it is easier to work with, like less computation intensive or requires less future efforts in sampling.

*Such that it is less sensitive to noise. See 'bias variance' tradeoff.

Your goal is to understand a relationship. This can be done by making a plot and observe a straight line (or in the case of more variables the line is a plane and you look at statistics in a table instead of a figure because plotting 10-D is not easy in our 3-D physical world).
The role of the p-values is to filter the effects from the noise (like you would do in the 2-D, plot to tell if there is a straight line relationship, or just noise). The p-values indicate statistical significance. That means that you have observed an effect that is statistically measurable with your experiment. The other effects, with high p-value, might be present but are overshadowed by noise.
So in your case it seems fine to go with p-values. However, be aware of the pitfalls with p-values.
