| City | Q | P | I | A | Psub | Pop |
| 1 | 32.92 | 1.89 | 32.4 | 4.38 | 2.08 | 98.7 |
| 2 | 28.51 | 1.94 | 29.9 | 4.12 | 2.06 | 104.5 |
| 3 | 33.94 | 1.99 | 29.1 | 5.06 | 2.15 | 105.1 |
| 4 | 33.45 | 2.04 | 28.6 | 5.36 | 2.18 | 106.9 |
| 5 | 35.68 | 2.09 | 30.2 | 5.38 | 2.18 | 108.9 |
| 6 | 29.01 | 1.89 | 31.4 | 3.47 | 1.99 | 115.2 |
| 7 | 30.68 | 1.94 | 30.3 | 4.06 | 2.05 | 117.8 |
| 8 | 43.14 | 1.99 | 30.4 | 5.29 | 2.17 | 120.2 |
| 9 | 20.84 | 2.04 | 28.6 | 3.39 | 1.98 | 120.8 |
| 10 | 31.27 | 2.09 | 26.3 | 5.12 | 2.16 | 122.7 |
| 11 | 35.45 | 1.89 | 26.5 | 4.44 | 2.09 | 122.9 |
| 12 | 32.14 | 1.94 | 29.5 | 4.18 | 2.06 | 124.6 |
| 13 | 43.02 | 1.99 | 29.1 | 5.57 | 2.2 | 125.8 |
| 14 | 34.73 | 2.04 | 31.2 | 3.57 | 2 | 131.9 |
| 15 | 43.14 | 2.09 | 23.8 | 6.43 | 2.39 | 133.1 |
| 16 | 33.17 | 1.89 | 28.8 | 3.95 | 2.04 | 135.6 |
| 17 | 41.59 | 1.94 | 29.3 | 4.04 | 2.05 | 136.5 |
| 18 | 32.1 | 1.99 | 27.4 | 4.36 | 2.08 | 141.8 |
| 19 | 45.74 | 2.04 | 27.8 | 5.81 | 2.23 | 144.8 |
| 20 | 35.92 | 2.09 | 30.2 | 4.63 | 2.11 | 150.1 |
I have collected real data on the sale of a microwavable cup of soup across 20 different cities for the same time period (a month). The variables in the dataset are:
Quantity sold in the city for that month: Measured in thousands of units
Price: measured in dollars
Average Income in the city: Measured in thousands of dollars
Ads: Average number of ads run in stores for that city during that month.
Price of a substitute product: measured in dollars
Population of the city: measured in thousands of people
The dataset is on Canvas and, using Excel or any other statistical software, please answer the following questions:
- Describe the patterns in quantity sold and own and rival prices during this time period using basic descriptive statistics. Graphs are welcome as well.
- Take the logs of the variables, and estimate the demand function.
- Interpret the R-square.
- Interpret the coefficients for logP and logPsub
- Interpret the p-values associated with each independent variable
- Are consumers price sensitive? Why or why not? (be as precise as you can – you have estimates!). Does this price sensitivity make sense given the good we are examining?
- How sensitive are our consumers to changes in the rival good’s price? Explain in detail.
- Suppose we decide to charge a per ounce price of .07, while at the same time our rival charges a price of .05. All else equal, what would you expect sales to be? How confident are you in your forecast? Explain.
- Suppose we are charging a price of .065 per ounce and our current marginal cost per ounce is .05. Are we maximizing profits at this price? If not, should we raise or lower price? Why?
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