Answer:
The residual points are (1,-3.3)(3,4.7)(5,0.7)(6,-1.8)(8,-0.8).
Step-by-step explanation:
Given : These are the values in Ariel’s data set. (1, 67), (3, 88), (5,97), (6, 101), (8, 115) Ariel determines the equation of a linear regression line to be y=6.5x+63.8 .
To find : Use the point tool to graph the residual plot for the data set. Round residuals to the nearest unit as needed.
Solution :
A residual is defined as the difference between the predicted value and the actual value.
We have given a linear regression line which gives you predicted output.
Actual output are given in a point.
So, Taking one by one to find the residual value.
1) (1,67)
Actual = 67
Predicted = y=6.5(1)+63.8=70.3
Residual = 67-70.3=-3.3
The residual at x = 1 is -3.3.
2) (3,88)
Actual = 88
Predicted = y=6.5(3)+63.8=83.3
Residual = 88-83.3=4.7
The residual at x = 3 is 4.7.
3) (5,97)
Actual = 97
Predicted = y=6.5(5)+63.8=96.3
Residual = 97-96.3=0.7
The residual at x = 5 is 0.7.
4) (6,101)
Actual = 101
Predicted = y=6.5(6)+63.8=102.8
Residual = 101-102.8=-1.8
The residual at x = 6 is -1.8.
5) (8,115)
Actual = 115
Predicted = y=6.5(8)+63.8=115.8
Residual = 115-115.8=-0.8
The residual at x = 8 is -0.8.
Therefore, The residual points are (1,-3.3)(3,4.7)(5,0.7)(6,-1.8)(8,-0.8).
