Economics for Managers: Pizza Demand
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Adam's Pizza operates in six cities in Texas and Oklahoma. The owner, Mr. Jenkins, wants to have an empirical estimation of the demand for his pizza. he plans to formulate pricing and promtion decisions for the next year, and he wants to know how price and advertising expenditure affect sales and use the results of the estimation to make a forecast for the next year (2016-1). He hired an economist to do a regression analysis. The economist collected data for 2012-2014 (quarterly) for sales, price, competitor's price, income, and population. this data is available in table 1 (link provided below).
The following regression model was fit to the collected data:
(see attached)
where P_x is the price of pizza, P_y is the competitor's price, A is advertising expenditure, Y is income, Pop is population, T is trend and n is a residual or error term. Least square regression results based on the data are provided in Table 2 (link provided below).
Write a report on the findings of the regression. Include the following elements:
- Describe the economic meaning and statistical significance of each independent variable.
- Interpret the coefficient of determination (R^2).
- Use a regression model and 2014 data to estimate 2015-1 sales.
- Derive 95% and 99% confidence intervals for the 2015-1 estimate.
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Solution Summary
Using the pizza restaurant example, a regression model with explanation is given.
Solution Preview
Please find the formatted Solution attached.
Price is important factor in order to see the demand of the product. Here we have two variables such as Price of Pizza and Price of the competitor's pizza which impact the demand of the Pizza which is our dependent variable and denoted as Q in the model.
We can see that the price of the pizza is a significant predictor of the dependent variable because its corresponding p-value is 0.0000 which is smaller than 5% level of significance. We can say that this variable which is Px (Price of the Pizza) is statistically significant at 5% level of significance. The coefficient of this variable is -122606.8172, it means if there is a one unit price increase, the demand will be reduced by 122606.9172 units.
We can see that the competitor price of the pizza is also significant predictor of the dependent variable because its corresponding p-value is 0.031956 which is smaller than 5% level of significance. We can say that this ...
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