Using the Excel file Forecasting Sales Using Ad Dollars Data use Excel to create a forecast of Sales $ using Ad $.
Analyze the data using simple linear regression in Excel to answer the following questions.
- Does more ad spending increase, decrease, or have no effect on sales $?
- What is the regression equation? Write it and its numerical values.
- Using the regression equation calculate predicted sales and error values and enter them into the Excel spreadsheet in the appropriate spaces.
Homework Answers
| X | Y | Y1=828.13+10.79X | E=Y1-Y | |||||||
| Ad $ (thousands) | Sales ($ million) | Predicted Sales | Error | 1 | ||||||
| 41 | 1,250 | 1,270 | 20 | |||||||
| 54 | 1,380 | 1,411 | 31 | |||||||
| 63 | 1,425 | 1,508 | 83 | |||||||
| 54 | 1,425 | 1,411 | (14) | |||||||
| 48 | 1,450 | 1,346 | (104) | |||||||
| 46 | 1,300 | 1,324 | 24 | |||||||
| 62 | 1,400 | 1,497 | 97 | |||||||
| 61 | 1,510 | 1,486 | (24) | |||||||
| 64 | 1,575 | 1,518 | (57) | |||||||
| 71 | 1,650 | 1,594 | (56) | |||||||
| Click "Data" | ||||||||||
| Click "Data Analysis" | ||||||||||
| Select "Regression" tool | ||||||||||
| Click"OK" | ||||||||||
| Input y_Range (Sales) | ||||||||||
| Input x_Range (Rental Ad $) | ||||||||||
| Click "OK" | ||||||||||
| SUMMARY OUTPUT | ||||||||||
| Regression Statistics | ||||||||||
| Multiple R | 0.847950033 | |||||||||
| R Square | 0.719019259 | |||||||||
| Adjusted R Square | 0.683896667 | |||||||||
| Standard Error | 67.19447214 | |||||||||
| Observations | 10 | |||||||||
| ANOVA | ||||||||||
| df | SS | MS | F | Significance F | ||||||
| Regression | 1 | 92431.72331 | 92431.72331 | 20.47170228 | 0.0019382 | |||||
| Residual | 8 | 36120.77669 | 4515.097086 | |||||||
| Total | 9 | 128552.5 | ||||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |||
| Intercept | 828.1268882 | 136.1285978 | 6.083415987 | 0.000294814 | 514.2137788 | 1142.04 | 514.2138 | 1142.04 | ||
| X Variable 1 | 10.7867573 | 2.384042146 | 4.52456653 | 0.0019382 | 5.289146253 | 16.28437 | 5.289146 | 16.28437 | ||
| Regression Equation : | ||||||||||
| Y=a+bX | ||||||||||
| a=Intercept= | 828.13 | |||||||||
| b=X Variable1= | 10.79 | |||||||||
| Y=828.13+10.79X | ||||||||||
| R Square value = | 0.719019259 | |||||||||
| This indicates reasonably good correlation between Ad$ and Sales | ||||||||||
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