The residuals of the linear regression equation are -1.36, 0.32, 0.01, 0.62, 0.17, -0.24, 0.89, 0.09, 0.18 and -0.7
<h3>How to determine the residuals?</h3>
The regression equation is given as:
y = 0.141x + 0.842
Next, we calculate the predicted values (y) at the corresponding x values.
So, we have:
y = 0.141 * 13.8 + 0.842 = 2.78
y = 0.141 * 18 + 0.842 = 3.38
y = 0.141 * 16.7 + 0.842 = 3.20
y = 0.141 * 18 + 0.842 = 3.38
y = 0.141 * 0.7 + 0.842 = 0.94
y = 0.141 * 21.9 + 0.842 = 3.93
y = 0.141 * 15.5 + 0.842 = 3.03
y = 0.141 * 9.2 + 0.842 = 2.14
y = 0.141 * 19.5 + 0.842 = 3.59
y = 0.141 * 16.7 + 0.842 = 3.20
The residuals are then calculated using:
Residual = Actual value - Predicted value
So, we have:
Residual = 1.42 - 2.78 = -1.36
Residual = 3.7 - 3.38 = 0.32
Residual = 3.21 - 3.20 = 0.01
Residual = 4 - 3.38 = 0.62
Residual = 1.11 - 0.94 = 0.17
Residual = 3.69 - 3.93 = -0.24
Residual = 3.92 - 3.03 = 0.89
Residual = 2.23 - 2.14 = 0.09
Residual = 3.77 - 3.59 = 0.18
Residual = 2.5 - 3.20 = -0.7
Hence, the residuals of the linear regression equation are -1.36, 0.32, 0.01, 0.62, 0.17, -0.24, 0.89, 0.09, 0.18 and -0.7
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is calculated from the sample, and used as an estimate for the population variance. Thus, it can be said that it represents both 362 and 5530 customers, and the correct option is D.
