In: Statistics and Probability
Answer is TRUE
let us consider the following data
| x | y |
| 61 | 77 |
| 43 | 47 |
| 39 | 46 |
| 66 | 81 |
| 66 | 76 |
| 64 | 75 |
| 44 | 49 |
| 77 | 95 |
| 58 | 72 |
| 77 | 91 |
The estimated regression of y ( dependent variable) on x (independent variable) is

Solving for a and b we get
a= - 6.15
b= 1.29
(Note : this example is just for explanation , so calculation is not shown )
Thus we get , estimate of regression equation is
-
6.15 +1.29 x
From this equation , for each x we will get a predicted value of y , and residual is given by

| x | y | ![]() |
![]() |
| 61 | 77 | 72.54 | 4.46 |
| 43 | 47 | 49.32 | -2.32 |
| 39 | 46 | 44.16 | 1.84 |
| 66 | 81 | 78.99 | 2.01 |
| 66 | 76 | 78.99 | -2.99 |
| 64 | 75 | 76.41 | -1.41 |
| 44 | 49 | 50.61 | -1.61 |
| 77 | 95 | 93.18 | 1.82 |
| 58 | 72 | 68.67 | 3.33 |
| 77 | 91 | 93.18 | -2.18 |
Thus residual is the difference between the actual value of dependent variable and value predicted by regression line .