These are the values in Priti’s data set.
2 answers:
Answer:
The residual points are (2,-1.2),(3,-2.2),(4,4.8),(5,1.8),(6,-3.2).
Step-by-step explanation:
Given : These are the values in Priti’s data set (2, 154), (3, 168), (4, 190), (5, 202), (6, 212) . Priti determines the equation of a linear regression line to be yˆ=15x+125.2 .
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 i.e. Residual=Actual - Predicted
We have given a linear regression line which gives you predicted output i.e. yˆ=15x+125.2
Now, we find the residual value.
1) (2,154)
Actual = 154
Predicted = y=15(2)+125.2=155.2
Residual =154-155.2= -1.2
The residual at x = 2 is -1.2.
2) (3,168)
Actual = 168
Predicted = y=15(3)+125.2=170.2
Residual =168-170.2= -2.2
The residual at x = 3 is -2.2.
3) (4,190)
Actual = 190
Predicted = y=15(4 )+125.2=185.2
Residual =190-185.2= 4.8
The residual at x = 4 is 4.8.
4) (5,202)
Actual = 202
Predicted = y=15(5)+125.2=200.2
Residual =202-200.2= 1.8
The residual at x = 5 is 1.8.
5) (6,212)
Actual = 212
Predicted = y=15(6)+125.2=215.2
Residual =212-215.2= -3.2
The residual at x = 6 is -3.2.
Therefore, The residual points are (2,-1.2),(3,-2.2),(4,4.8),(5,1.8),(6,-3.2).
Refer the attached figure below showing the residual points.
You might be interested in