A manager of a pizza outlet has collected monthly sales (in $000) data for some time in an attempt to predict sales for the outlet. During this time, the outlet has been running a series of different advertisements. The manager has kept track of X1, the number of advertisements that have appeared as well as X2, the cost of these advertisements (in hundreds of dollars) for each month.
The data are given. The initial model is Sales = Bo + B1*X1 + B2*X2. Consider at least three different models, determine a "best" model for predicting Sales, and explain why each is or is not your "best".. Feel free to transform any of the variables and use them in your models. Use your "best" model to estimate Sales for May, 2014, if there are to be 10 ads purchased for a total cost of $1500.
| Month |
No. of Ads |
Cost of Ads |
Sales |
| Jan-13 |
11 |
14 |
49.4 |
| Feb-13 |
8 |
11.8 |
47.5 |
| Mar-13 |
11 |
15.7 |
52.6 |
| Apr-13 |
14 |
15.5 |
49.3 |
| May-13 |
17 |
19.5 |
61.1 |
| Jun-13 |
15 |
16.8 |
53.2 |
| Jul-13 |
12 |
12.8 |
47.4 |
| Aug-13 |
10 |
13.6 |
49.4 |
| Sep-13 |
17 |
18.2 |
62 |
| Oct-13 |
11 |
16 |
47.9 |
| Nov-13 |
8 |
13 |
47.3 |
| Dec-13 |
18 |
20 |
61.5 |
| Jan-14 |
12 |
15.1 |
54.2 |
| Feb-14 |
10 |
14.2 |
44.7 |
| Mar-14 |
13 |
17.3 |
53.6 |
| Apr-14 |
12 |
15.9 |
55.4 |