# Should I remove data with known computational error before doing linear regression?

I have a dataset having 252 observations. There are two variables having a defined and known linear relationship. By computation, I found 4 observations do not follow the linear equation, and I conclude the difference is due to data entry errors.

Although those two variables are not used in my regression model, I am still concerned the reliability of those 4 observations.

My question is should I remove those 4 observations from the dataset, before doing a linear regression?

EDIT:

There are two variables "body fat" and "density". The value of "body fat" is computed by "density" using a linear formula, stated by the creators of this dataset. I found those 4 entries are far off from the given formula, whereas all others are exactly follow the formula.

• How did you decide that those 4 entries do not follow the linear equation? They may have occurred by chance due to random fluctuation. If you did a statistical test to show that they were unlikely to occur by chance then you have justification for removing them.
– Hugh
Feb 17, 2017 at 22:42
• @Hugh Sorry it's my bad to make the confusion. There are two variables "body fat" and "density". The value of "body fat" is computed by "density" using a linear formula, stated by the creators of this dataset. I found those 4 entries are far off from the formula, whereas all others are exactly follow the formula. Feb 17, 2017 at 22:46
• Well if all the other entries exactly follow the formula then there is 0 error in your linear regression so a statistical test for those other 4 entries will find that they cannot occur by chance. In this case you are justified in removing those 4 entries.
– Hugh
Feb 18, 2017 at 7:27
• If there is a little error in your linear regression and the entries do not exactly follow the linear relationship then maybe it is obvious from a graph that those 4 entries are wrong. Looking at the graph isn't a formally correct way to make a decision but it can be good enough for many purposes.
– Hugh
Feb 18, 2017 at 7:30