# Reasons for fitting results (anti image)

I need to interpret a residual table which was fitted by an analyst plotting $y_.0123$ (final residual) against the $x_{1.023}$. The table represents residuals produced by each variable.

data
pts  Y_.0123 Bo     B1      B2      B3
212  small   small  small   small   small
14   small   small  large   small   small
10   small   small  small   large   small
2    small   small  small   small   large
2    small   large  small   small   small
2    large   large  large   large   small


I need to interpret the table and make suggestions. It is an appropriate assumption to state that the small residuals should remain in the model since they are relative to the $y$. The last 3 rows with data point of 2 should be removed since they are causing residuals to be possible outliers or can be data errors. Also removing B1 and B2 will improve since they will eliminate the large residuals remaining. What beta will be affected if I remove B1 and B2?

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