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Making Decisions Based On Demands Essay

1234 words - 5 pages

Making Decisions Based on Demand and Forecasting
Latonya Woodrow
Instructor Name: Dr.Samuel F. Onipede
ECO 550 –Managerial Economics and Globalization
July 21, 2013

College Students buy pizza in large quantities for a cheap price, but if the prices were to increase, then these same students may look for similar alternatives that will not empty their wallets.   These are possible alternatives that offer a large quantity of food at a reasonable price that can affect the demand of pizza.   However, monitoring the costs of the ...view middle of the document...

Considering there are 365 days in a year we can assume that nine households are eating pizza each day, with an average of 3 people in the household let’s assume they normally order two pizzas, a total of 18 pizzas per day.   The average income of a household in Morehead City is \$28,737 and the average price of a Domino’s Pizza is \$7.99 for a large (with no coupons or specials).   We also know based on the city data collected that the population is growing at a rate of 1.4% per year, which is a positive and important aspect to a prospective business for growth when considering the area for location.

Using a regression formula, the formula is based on:
x = years (time period)
y = trend forecast for the time period assuming a 1.4% increase due to population growth
a = intercept (at period 0)
b = slope of the line
r2 = coefficient determinant

Using excel and inputting the data for x and y (using the basic method of only one independent and one dependent variable) we can solve for r2, the coefficient determinant.   See below:

| Domino’s Pizza = 8 Year Growth Forecast based on Quantity Demanded | | | |
| | | | | | | | |
Regression Statistics | | | | | | | |
Multiple R | 0.847804172 | | | | | | | |
R Square | 0.718771915 | | | | | | | |
Adjusted R Square | 0.671900567 | | | | | | | |
Standard Error | 800.2039299 | | | | | | | |
Observations | 8 | | | | | | | |
| | | | | | | | |
ANOVA | | | | | | | | |
| df | SS | MS | F | Significance F | | | |
Regression | 1 | 9819401.524 | 9819401.524 | 15.33499573 | 0.007838086 | | | |
Residual | 6 | 3841957.976 | 640326.3294 | | | | | |
Total | 7 | 13661359.5 |   |   |   | | | |
| | | | | | | | |
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% |
Intercept | -966174.012 | 248985.7782 | -3.880438549 | 0.00816655 | -1575420.263 | -356927.76 | -1575420.26 | -356927.76 |
X Variable 1 | 483.5238095 | 123.474147 | 3.915992304 | 0.007838086 | 181.3934558 | 785.654163 | 181.393456 | 785.654163 |
| | | | | | | | |
| | | | | | | | |
| | | |
RESIDUAL OUTPUT | | | |
| | | |
Observation | Predicted Y | Residuals | |
1 | 7159.416667 | -589.4166667 | |
2 | 7642.940476 | -981.9404762 | |
3 | 8126.464286 | 1136.535714 | |
4 | 8609.988095 | 782.0119048 | |
5 | 9093.511905 | 429.4880952 | |
6 | 9577.035714 | 88.96428571 | |
7 | 10060.55952 | -259.5595238 | |
8 | 10544.08333 | -606.0833333 | |
| | | | | | | | |
| | | | | | | | |
Interpreting the coefficient determinant will influence whether or not to open the pizza business.   The r-squared tells us that we have explained 72% in the regression of what will affect demand for a new pizza business to be profitable in the new area.   The position coefficient shows that as the independent variable changes the quantity demanded changes in the same direction.
Using the data collected on Morehead City in another example:

| Domino’s Pizza = Growth Forecast based on Pizza...

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