In: Math
Consider the following sample data for the relationship between advertising budget and sales for Product A: Observation 1 2 3 4 5 6 7 8 9 10
Advertising ($) 60,000 70,000 70,000 80,000 80,000 90,000 100,000 100,000 110,000 110,000
Sales ($) 363,000 432,000 417,000 502,000 483,000 537,000 614,000 625,000 653,000 666,000
What is the slope of the "least-squares" best-fit regression line? Please round your answer to the nearest hundredth.
Here we have data:
| Observation | Advertising | Sales |
| 1 | 60000 | 363000 |
| 2 | 70000 | 432000 |
| 3 | 70000 | 417000 |
| 4 | 80000 | 502000 |
| 5 | 80000 | 483000 |
| 6 | 90000 | 537000 |
| 7 | 100000 | 614000 |
| 8 | 100000 | 625000 |
| 9 | 110000 | 653000 |
| 10 | 110000 | 666000 |
Scatter plot:

Slop of the regression line = 6.02
| Regression Statistics | ||||||||
| Multiple R | 0.99489 | |||||||
| R Square | 0.989806 | |||||||
| Adjusted R Square | 0.988531 | |||||||
| Standard Error | 11456.71 | |||||||
| Observations | 10 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 1 | 1.02E+11 | 1.02E+11 | 776.7521 | 2.97E-09 | |||
| Residual | 8 | 1.05E+09 | 1.31E+08 | |||||
| Total | 9 | 1.03E+11 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 5156.584 | 19148.8 | 0.26929 | 0.794522 | -39000.6 | 49313.8 | -39000.6 | 49313.8 |
| X Variable 1 | 6.02 | 0.216126 | 27.87027 | 2.97E-09 | 5.5251 | 6.521875 | 5.5251 | 6.521875 |