Making Decisions Based on Demand
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 | |
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 | |