Making Decisions Based on Demand

Topics: Supply and demand, Statistics, Price elasticity of demand Pages: 5 (1393 words) Published: August 15, 2013
Assignment 1
Making Decisions Based on Demand and Forecasting
July 22, 2013

Using the sample data: The Demand for Pizza, (shown below) I will conduct a demand analysis and forecast for pizza. Through this analysis, I make a decision whether Domino’s should establish a presence in the community depicted in the sample data. The sample data included one dependent variable (Y) Quantity demanded and three independent variables (X1) price of pizza (X2) Tuition (X3) Price of Soft drinks and (4) Location 1 for urban and 0 for otherwise. This data included 30 observations. Table 1.1 Sample Data: The Demand for Pizza|

| | | | | |
College| Y| X1| X2| X3| X4|
1| 10| 100| 14| 100| 1|
2| 12| 100| 16| 95| 1|
3| 13| 90| 8| 110| 1|
4| 14| 95| 7| 90| 1|
5| 9| 110| 11| 100| 0|
6| 8| 125| 5| 100| 0|
7| 4| 125| 12| 125| 1|
8| 3| 150| 10| 150| 0|
9| 15| 80| 18| 100| 1|
10| 12| 80| 12| 90| 1|
11| 13| 90| 6| 80| 1|
12| 14| 100| 5| 75| 1| X1 - Price of Pizza| |
13| 12| 100| 12| 100| 1| X2 - Tuition| |
14| 10| 110| 10| 125| 0| X3 - Price of soft drinks|
15| 10| 125| 14| 130| 0| X4 - Location 1 for urban 0 for otherwise| 16| 12| 110| 15| 80| 1| Y= Quantity Demanded|
17| 11| 150| 16| 90| 0|
18| 12| 100| 12| 95| 1|
19| 10| 150| 12| 100| 0|
20| 8| 150| 10| 90| 0|
21| 9| 150| 13| 95| 0|
22| 10| 125| 15| 100| 1|
23| 11| 125| 16| 95| 1|
24| 12| 100| 17| 100| 0|
25| 13| 75| 10| 100| 1|
26| 10| 100| 12| 110| 1|
27| 9| 110| 6| 125| 0|
28| 8| 125| 10| 90| 0|
29| 8| 150| 8| 80| 0|
30| 5| 150| 10| 95| 0|
| | | | | |
I used Excel to calculate an estimated regression. The resulting table is shown below. I have highlighted the results I used to form my decision and recommendations.

Table 1.2 - Pizza Regression| | | | |
| | | | |
Regression Statistics| | | |
Multiple R| 0.84649854| | | |
R Square| 0.716559778| | | |
Adjusted R Square| 0.671209343| | | |
Standard Error| 1.640478718| | | |
Observations| 30| | | |
| | | | |
ANOVA| | | | |
 | df| SS| MS| F|
Regression| 4| 170.087406| 42.521852| 15.800505|
Residual| 25| 67.27926063| 2.6911704| |
Total| 29| 237.3666667|  |  |
| | | | |
 | Coefficients| Standard Error| t Stat| P-value|
Intercept| 26.66685004| 3.278084646| 8.1348876| 1.73E-08| X1| -0.087750505| 0.018062008| -4.858292| 5.379E-05|
X2| 0.138204092| 0.086646076| 1.5950416| 0.1232685|
X3| -0.075895058| 0.019225627| -3.947599| 0.0005667|
X4| -0.544278595| 0.884620096| -0.615268| 0.5439383|

Examining the Coefficient of Determination
The first result I examined was the R Square or Coefficient of Determination. The value expressed in the Regression Statistics shown in the table above is 0.716559788. This indicates that roughly 72% of the variation in the quantity of pizza demanded can be explained in the variables used in this analysis. Since 72% of the variation in the quantity of pizza demanded can be explained in the variables used, I determined that other results found in this study would be helpful in deciding whether or not to open a pizza business. Examining P-Values

In order to test the statistical significance of the variables and the regression equation, I examined the P-Values for each variable in the regression study. The most statistically significant variable is X1 or price of pizza, followed by the price of soft drinks. Both X1 and X3 have a p value of less than .05. This means that there is more than 95% confidence that these variables are what drive the demand for pizza in this study. Based on our definition of statistical significance, a small p-value as observed here means that we wouldn’t observe what we...
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