I have to perform a linear regression on a dataset. However, I am having trouble figuring out what type of imputation I should do on the data because in some cases the majority of the the data is missing, or at least 50% is.
Also, the data has 20,000 plus rows.
I am considering two options as of now:
- Do you all suggest I replace every NA with the mean?
- Or should I just drop all NAs which will reduce my data significantly.
By the way, I do have to run a linear regression on all the variables, and then again on each of my variables singularly.
The problem with second option is, I can see how dropping all of the NAs mislead the data.
Is there because replacing with the mean can oversaturate the data?
Just want to hear the feedback from the community.