ABC Limited is a small company that produces premium toys for pets. It assumes that sales depend on the number of advertisements. The table below (named "ABC Limited") provides 50 sample data points (from the past) that show the relationship between the number of advertisements and sales of the company made. The company would like to determine if the number of advertisements impacts Sales and if so, by how much. Using excel, run a regression analysis (at a confidence level of 95%) for the data table given below. Based on the output you get, answer the following three questions: 1a. Provide an interpretation of the value you received for Multiple R, R-square, and Adjusted R-square. Explain what they mean in this context. 1b. Create a regression equation based on the output. Explain what does b0 and b1 imply in this context. Explain what Significance F and P-value for the X variable 1 imply in this context. 2). Assume that the company wants to use 34 (number of ) advertisements for next week. Use the regression equation/model developed in question (1) to estimate the total sales. 3). Now, based on the calculation you made to answer question (2) above for 34 motors: (a) compare that estimated sale value (you calculated above) with the value your excel gave you under the "Residual Output"; explain what "Predicted Y" and "Residuals" mean by; explain if your calculation in question (2) was correct. (b) also, compare that estimated sales value with the sales value given for the third observation of the original data table below (as the third observation has 34 number of advertisements) - in other words, discuss if you find any difference between your calculated value on question 2 with that of the value given for 34 number of advertisements in the original table. Explain your findings. Note: You may want to review the last 8 minutes of the video named "Calculating Simple Linear Regression Analysis" under Module 5 to answer questions 3a and 3b. ABC Limited Number of Advertisements Sales 28 $ 7,531 27 $ 6,329 34 $ 8,413 30 $ 7,793 19 $ 5,360 21 $ 4,838 11 $ 2,551 17 $ 3,899 33 $ 8,326 23 $ 5,465 7 $ 2,283 23 $ 5,413 18 $ 4,238 26 $ 6,911 20 $ 6,315 29 $ 8,243 10 $ 2,866 22 $ 6,775 14 $ 4,289 6 $ 1,475 15 $ 3,590 35 $ 9,439 23 $ 6,760 18 $ 5,170 31 $ 7,780 21 $ 4,896 35 $ 8,816 29 $ 8,116 21 $ 6,212 24 $ 5,551 27 $ 7,080 36 $ 9,826 24 $ 6,129 20 $ 5,094 13 $ 3,568 14 $ 3,738 19 $ 5,332 14 $ 3,286 28 $ 6,664 24 $ 5,990 27 $ 7,093 16 $ 3,975 36 $ 9,046 20 $ 4,906 19 $ 5,324 7 $ 2,734 32 $ 8,138 21 $ 5,376 29 $ 7,763 23 $ 5,964
ABC Limited is a small company that produces premium toys for pets. It assumes that sales depend on the number of advertisements. The table below (named "ABC Limited") provides 50 sample data points (from the past) that show the relationship between the number of advertisements and sales of the company made. The company would like to determine if the number of advertisements impacts Sales and if so, by how much. Using excel, run a
1a. Provide an interpretation of the value you received for Multiple R, R-square, and Adjusted R-square. Explain what they mean in this context.
1b. Create a regression equation based on the output. Explain what does b0 and b1 imply in this context. Explain what Significance F and P-value for the X variable 1 imply in this context.
2). Assume that the company wants to use 34 (number of ) advertisements for next week. Use the regression equation/model developed in question (1) to
estimate the total sales.
3). Now, based on the calculation you made to answer question (2) above for 34 motors:
(a) compare that estimated sale value (you calculated above) with the value your excel gave you under the "Residual Output"; explain what "Predicted Y" and "Residuals" mean by; explain if your calculation in question (2) was correct.
(b) also, compare that estimated sales value with the sales value given for the third observation of the original data table below (as the third observation has 34 number of advertisements) - in other words, discuss if you find any difference between your calculated value on question 2 with that of the value given for 34 number of advertisements in the original table. Explain your findings. Note: You may want to review the last 8 minutes of the video named "Calculating Simple Linear Regression Analysis" under Module 5 to answer questions 3a and 3b.
| ABC Limited | |
| Number of Advertisements | Sales |
| 28 | $ 7,531 |
| 27 | $ 6,329 |
| 34 | $ 8,413 |
| 30 | $ 7,793 |
| 19 | $ 5,360 |
| 21 | $ 4,838 |
| 11 | $ 2,551 |
| 17 | $ 3,899 |
| 33 | $ 8,326 |
| 23 | $ 5,465 |
| 7 | $ 2,283 |
| 23 | $ 5,413 |
| 18 | $ 4,238 |
| 26 | $ 6,911 |
| 20 | $ 6,315 |
| 29 | $ 8,243 |
| 10 | $ 2,866 |
| 22 | $ 6,775 |
| 14 | $ 4,289 |
| 6 | $ 1,475 |
| 15 | $ 3,590 |
| 35 | $ 9,439 |
| 23 | $ 6,760 |
| 18 | $ 5,170 |
| 31 | $ 7,780 |
| 21 | $ 4,896 |
| 35 | $ 8,816 |
| 29 | $ 8,116 |
| 21 | $ 6,212 |
| 24 | $ 5,551 |
| 27 | $ 7,080 |
| 36 | $ 9,826 |
| 24 | $ 6,129 |
| 20 | $ 5,094 |
| 13 | $ 3,568 |
| 14 | $ 3,738 |
| 19 | $ 5,332 |
| 14 | $ 3,286 |
| 28 | $ 6,664 |
| 24 | $ 5,990 |
| 27 | $ 7,093 |
| 16 | $ 3,975 |
| 36 | $ 9,046 |
| 20 | $ 4,906 |
| 19 | $ 5,324 |
| 7 | $ 2,734 |
| 32 | $ 8,138 |
| 21 | $ 5,376 |
| 29 | $ 7,763 |
| 23 | $ 5,964 |
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