Domino's

Making Decisions Based on Demand and Forecasting

Managerial Economics

April 29, 2013

The number of pizzas (dependent variable) were considered using the following independent variables: price per pizza, price for a 20 ounce soda, the average yearly income of a family of four and the population number in each of the surroundings communities in Southeastern Pennsylvania.

These four independent variables were considered as they would provide some direct correlation to the dependent variable of number of pizzas when looking at price of a slice and a soda. Also, macro factors such as population and income levels helped give a broader picture. The data is attached and consisted of a sample of 9 operations. It should be noted that gathering the number of pizzas sold was a guesstimated based on the areas being sampled.

While a bigger sample would produce better numbers, this sample provided a good basis for the decision making. When the regression was run, the coefficient of determination was calculated to be .9098, indicating that over 90% of the change in the number of pizzas can be explained by each of the independent variables mentioned above. Also, the correlation coefficient was very high at .8197, indicating a strong relationship between the number of pizzas and the four independent variables.

It should not be assumed that the independent variables cause the change in the number of pizzas, instead the relationship is strong, which is a strong contribution to the statistical significance of the model. In addition the standard of error may seem high at 476.1124, but when compared to the intercept of 25,358.021, that number is low. Also, the obtained F statistical value is 10.0899, which id higher than the critical value of 3.83785. Also, when analyzing all of the independent variables to another, it appears that the price per slice is not as strong in relationship to the others, while the income...