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linear_regression_w2.xlsxRegression Line
| Calculation of the Regression Line (amounts in thousands) | ||||||||||
| Advertising (x) | Sales (y) | xy | x2 | y2 | ||||||
| 9 | 15 | 135 | 81 | 225 | ||||||
| 19 | 20 | 380 | 361 | 400 | ||||||
| 11 | 14 | 154 | 121 | 196 | ||||||
| 14 | 16 | 224 | 196 | 256 | ||||||
| 23 | 25 | 575 | 529 | 625 | ||||||
| 12 | 20 | 240 | 144 | 400 | ||||||
| 12 | 20 | 240 | 144 | 400 | ||||||
| 22 | 23 | 506 | 484 | 529 | ||||||
| 7 | 14 | 98 | 49 | 196 | ||||||
| 13 | 22 | 286 | 169 | 484 | ||||||
| 15 | 18 | 270 | 225 | 324 | ||||||
| 17 | 18 | 306 | 289 | 324 | ||||||
| SUMS (S) | 174 | 225 | 3,414 | 2,792 | 4,359 | |||||
| Using the formulas below, we substitute from the table above and obtain an intercept (a) of 10.5836 and a slope (b) of .5632 | ||||||||||
| y= | a + bx | |||||||||
| a= | 10.5836431227 | |||||||||
| b= | 0.563197026 |
5632
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