In: Statistics and Probability
Based on the data shown below, calculate the regression line
(each value to two decimal places)
y =  x +
| x | y | 
|---|---|
| 1 | 27.54 | 
| 2 | 27.73 | 
| 3 | 23.42 | 
| 4 | 23.21 | 
| 5 | 22.4 | 
| 6 | 21.29 | 
| 7 | 19.18 | 
| 8 | 18.37 | 
| 9 | 16.66 | 
| 10 | 15.45 | 
| 11 | 14.34 | 
| 12 | 10.83 | 
| 13 | 11.52 | 
| 14 | 9.21 | 
| 15 | 5.5 | 
The following data are passed:
| X | Y | 
| 1 | 27.54 | 
| 2 | 27.73 | 
| 3 | 23.42 | 
| 4 | 23.21 | 
| 5 | 22.4 | 
| 6 | 21.29 | 
| 7 | 19.18 | 
| 8 | 18.37 | 
| 9 | 16.66 | 
| 10 | 15.45 | 
| 11 | 14.34 | 
| 12 | 10.83 | 
| 13 | 11.52 | 
| 14 | 9.21 | 
| 15 | 5.5 | 
The independent variable is X, and the dependent variable is Y. In order to compute the regression coefficients, the following table needs to be used:
| X | Y | X*Y | X2 | Y2 | |
| 1 | 27.54 | 27.54 | 1 | 758.4516 | |
| 2 | 27.73 | 55.46 | 4 | 768.9529 | |
| 3 | 23.42 | 70.26 | 9 | 548.4964 | |
| 4 | 23.21 | 92.84 | 16 | 538.7041 | |
| 5 | 22.4 | 112 | 25 | 501.76 | |
| 6 | 21.29 | 127.74 | 36 | 453.2641 | |
| 7 | 19.18 | 134.26 | 49 | 367.8724 | |
| 8 | 18.37 | 146.96 | 64 | 337.4569 | |
| 9 | 16.66 | 149.94 | 81 | 277.5556 | |
| 10 | 15.45 | 154.5 | 100 | 238.7025 | |
| 11 | 14.34 | 157.74 | 121 | 205.6356 | |
| 12 | 10.83 | 129.96 | 144 | 117.2889 | |
| 13 | 11.52 | 149.76 | 169 | 132.7104 | |
| 14 | 9.21 | 128.94 | 196 | 84.8241 | |
| 15 | 5.5 | 82.5 | 225 | 30.25 | |
| Sum = | 120 | 266.65 | 1720.4 | 1240 | 5361.9255 | 
Based on the above table, the following is calculated:





Therefore, based on the above calculations, the regression coefficients (the slope m, and the y-intercept n) are obtained as follows:


Therefore, we find that the regression equation is:
Y = 29.57 - 1.47 X