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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. That reduces down to 
. That is slope-intercept form. y = mx + b.