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Assignment 1: Making Decisions Based on Demand and Forecasting
Assignment 1 Demand Estimation
ECO 550 Managerial Economics and Globalization
Strayer University
Assignment 1: Making Decisions Based on Demand and Forecasting

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Assignment 1: Making Decisions Based on Demand and Forecasting
1. Using Excel or other calculation software, input the data you collected in criterion one to calculate
an estimated regression. Then, from the calculation provided, interpret the coefficient of
determination, indicating how it will influence your decision to open the pizza business. Explain any
additional variables that may improve the coefficient of determination.
When we analyze the independent variable as pizza price and pizza sale (dependent
variable), so we are getting:
Table 1.1
Price per pizza ($) P | sales per day (quantity) Y | promotional expenditures per day ($) A |
disposable income (M) ($) M |
5.99 | 90 | 130 | 37.5 |
6.99 | 80 | 120 | 38.5 |
7.99 | 70 | 110 | 39.5 |
8.99 | 60 | 100 | 40.5 |
9.99 | 50 | 90 | 41.5 |
A linear demand model would be specified as follows:
Q=a+B1A+B2P+B3M+e
But for our case we will take a simple linear regression model, so we will limited the section to the
simplest case of one independent and one dependent variable, where the form of the relationship
between the two variables is linear:
Y=a+bX
So instead of X we put the data from the table: P, M, A.

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Assignment 1: Making Decisions Based on Demand and Forecasting
Although there are several methods for determining the values of a and b (that is, finding the
regression equation that provides the best fit to the series of observations), the best known and most
widely used is the method of least squares. b is a slope and a is an intercept. Putting all the data in
Microsoft Excel we are getting the slope = -10 and intercept = 149.9 (for the pizza sale P and the
price of pizza Y dates only). Table 1.2
Table 1.2
Price per pizza ($) P | sales per day (quantity) Y |
5.99 | 90 |
6.99 | 80 |
7.99 | 70 |
8.99 | 60 |
9.99 | 50 |
Table 2.1 shows us that the less the price, the more demand for the pizza. So we can also say that
the price is elastic in the pizza case.
Table 2.1. That is what we are getting for the regression equation line:
Table 3.1. That is what we are getting after the calculation:
Price per pizza ($) | sales per day (quantity) |
5.99 | 90 |

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Anonymous
Just what I was looking for! Super helpful.

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