Homework SourseCostco sells paperback books in their retail stores and want
Costco sells paperback books in their retail stores and wanted to examine the relationship between price and demand. The price of a particular novel was adjusted each week and the weekly sales were recorded in the table below.
Sales
price
3
$12
4
$11
6
$10
10
$9
8
$8
10
$7
Management would like to use simple regression analysis to estimate weekly demand for this novel using the price of the novel.The average weekly sales for the novel when priced at $9 is
5.32
4.70
6.22
7.56
Mangement would like to use simple regression analysis to estimate weekly demand for this novel using the price of the novel.The coefficient of determination for this sample is
.336
.830
.881
.624
The correlation coefficient for this data is
.197
-.910
-.402
-.623
Management would like to use simple regression analysis to estimate weekly demand for this novel using the price of the novel.The slope for the regression is
-1.457
-.920
-.584
-.675
| Sales | price |
| 3 | $12 |
| 4 | $11 |
| 6 | $10 |
| 10 | $9 |
| 8 | $8 |
| 10 | $7 |
Solution
Performing the regression analysis using data analysis add-in in MS EXCEL gives following results.
Thus the regression equation will be given by,
Sales = -1.45714*Price + 20.67619
Now we can answer all the questions.
Management would like to use simple regression analysis to estimate weekly demand for this novel using the price of the novel.The average weekly sales for the novel when priced at $9 is
Sales = -1.45714*9 + 20.67619 = 7.56
Mangement would like to use simple regression analysis to estimate weekly demand for this novel using the price of the novel.The coefficient of determination for this sample is
R2 is the co-eficient of determination which in this case is 0.830
The correlation coefficient for this data is
Using the CORREL function in MS -EXCEL we get the correlation coefficient as -.910
Management would like to use simple regression analysis to estimate weekly demand for this novel using the price of the novel.The slope for the regression is
As can be seen from the regression analysis output , the slope is -1.457
| SUMMARY OUTPUT | |||||
| Regression Statistics | |||||
| Multiple R | 0.910376 | ||||
| R Square | 0.828784 | ||||
| Adjusted R Square | 0.78598 | ||||
| Standard Error | 1.385297 | ||||
| Observations | 6 | ||||
| ANOVA | |||||
| df | SS | MS | F | Significance F | |
| Regression | 1 | 37.15714 | 37.15714 | 19.36228 | 0.011689 |
| Residual | 4 | 7.67619 | 1.919048 | ||
| Total | 5 | 44.83333 |
