Solution:
Given the table below:
whose line of best fit is expressed as

The observed amount spent on entertainment is the value of y for a corresponding value of x in the table of value.
The predicted amount spent on entertainment is the value of y for a corresponding value of x in the equation of line of best fit.
The residual is the difference between the observed amount and predicted amount spent on entertainment.
Thus, given:


Hence,