ECO 550 Assignment 1 Making Decisions Based on Demand and Forecasting
ECO 550 Assignment 1 Making Decisions Based on Demand and Forecasting fin571
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Date Created: 11/11/15
Running Head DEMAND AND FORECASTING Making Decisions Based on Demand and Forecasting Name of the writer Name of the institution Demand and Forecasting 2 Making Decisions Based on Demand and Forecasting The demographics used for the demand analysis are the average yearly income of the house hold in Georgia the total yearly population and average kids per house The rationale behind choosing these demographics is that the demand is highly associated with the average income and can have a great impact on the demand of the economy for higher the income the higher the spending ability of an average house hold Therefore it can also be said that the average income is directly proportional to the spending ability of an average house hold whereas as far as total yearly population is concerned demand is also associated with the total population as for demand arises with rise in population Average kids per house hold also have a strong link with demand Considering the fact that pizza is highly popular among kids and is the cause of its major demand The other independent variables used for conducting a demand analysis are price of the pizza and price of the soda The rationale behind choosing these demographics is that the demand is also highly associated with price as per the demand and supply law the lower the price the higher the demand and the higher the price the lower the demand Pizza and soda are two main products of a pizza restaurant and its prices can have a great impact on the overall demand for it The dependent variable used was an yearly forecasted demand for pizza with respect to the various independent variables mentioned above which are yearly average income yearly total population average kids per house hold price of the soda and price Demand and Forecasting 3 of the pizza Deman Price of Price of Average Income Per Populatio kids per d Pizza Soda House Hold house 10000 10 05 500 9655252 4 20000 10 05 510 9592624 4 30000 8 05 505 9573434 4 40000 9 04 505 9541108 2 50000 9 04 540 9555900 3 60000 8 04 520 9601349 5 70000 7 03 560 9538657 3 80000 6 03 580 9564907 4 90000 5 03 600 9656019 4 100000 6 03 621 9563691 5 110000 4 03 690 9567481 5 120000 4 02 650 9539521 4 130000 3 02 640 9571551 2 140000 2 05 700 9650214 4 150000 3 05 780 9652174 3 160000 4 06 750 9655122 4 170000 3 03 790 9676306 3 180000 2 02 800 9503065 4 190000 2 02 810 9522233 4 200000 2 02 820 9629125 5 The data in the above figure was input in SPSS and linear regression analysis was applied to calculate an estimated regression taking demand as the dependent variable and the rest as independent variables The following output was generated Demand and Forecasting 4 Variables EnteredRemoved b Variables Variables Model Entered Removed Method 1 Average kids rice of Soda verage Enter ncome total opulation rice of Pizza a a All requested variables entered b Dependent Variable D Model Summary Std Error Change Statistics Mode R Adjusted of the R Square F Sig F l R Square R Square Estimate Change Change df1 df2 Change 1 988 a 976 967 10717670 976 112985 5 14 000 a Predictors Constant kids PS In pop PP Demand and Forecasting 5 ANOVA b Sum of Model Squares df Mean Square F Sig 1 Regression 6489E10 5 1298E10 112985 000 2 Residual 1608E9 14 1149E8 Total 6650E10 19 a Predictors Constant kids PS In pop PP b Dependent Variable D Coefficients a IU Standardized nstandardized Coefficients Coefficients Model B Std Error Beta t Sig 1 Constant 25144293 572525126 044 966 P 39 f me 0 6283420 2385538 303 2634 020 Pizza Price of 32033206 28144482 069 1138 274l Soda Average 343623 55074 675 6239 000 Income Population 004 060 004 074 942 A kiZnge 806476 2792121 012 289 777 a Dependent Variable Demand for pizza From the output generated the linear regression equation for the demand can be computed as D 25144293 6283420 Price of Pizza 32033206 Price of Soda 343623 Average Income 0004 Population 806474 Average kids Demand and Forecasting 6 The interpretation of each independent variable coefficient is as following The coefficient of price of pizza is 6283420 which shows that price of pizza has a negative impact on demand as for each unit dollar of increase in price of the pizza there would be a negative impact of 6283420 decrease in the demand of pizza The coefficient of price of soda is 32033206 which shows that price of soda has a negative impact on demand as for each unit dollar of increase in price of the soda there would be a negative impact of 32033206 decrease in the in the demand of pizza The coefficient of Average income per house hold is 343623 which shows that Average income per house hold has a positive impact on demand as for each unit dollar of increase in Average income per house hold there would be a negative impact of 343623 increase in the in the demand of pizza The coefficient of total population is 0004 which shows that total population almost has no impact on demand as for each unit increase in population there would just be a negative impact of 0004 decrease in the in the demand of pizza The coefficient of average kids per house hold is 806474 which shows that total population almost has no impact on demand as for each unit increase in average kids there would just be a negative impact of 806474 decrease in the in the demand of pizza The coefficient of determination is 0976 which shows that the overall dependency of the independent variables in the model is almost 98 percent and thus proves the efficiency of the model and there is no need for any further variables to improve the coefficient of determination The model can be used to forecast demand in Demand and Forecasting 7 the long term and because of its efficiency it can help us determine whether to open up for pizza business or not To test the statistical significance of the model we will calculate the demand for the 2nd year using the regression equation Taking price of pizza 10 price of soda 05 Average Income per House Hold 510 population 9592624 and average kids per home 4 and putting the values into the equation D 25144293 6283420 10 32033206 05 343623 510 0004 9592624 806474 4 Demand is calculated as 29656 The demand forecasted for the second year was 20000 whereas using the model the demand came out to be almost 30000 which is much more than anticipated and has a positive impact on the decision to open up the pizza business We will calculate the demand for the next four months now by taking price of pizza 10 price of soda 05 Average Income per House Hold 500 population 9655252 and average kids per home 4 and putting the values into the equation D 25144293 6283420 10 32033206 05 343623 500 0004 9655252 806474 4 Demand is calculated as 26419 for the 1st year And for the 1st four months it can be manipulated as 8806 Based on the forecasted demand for the next four months which is 8806 generating sales of 88060 is not a bad investment Also the fact that the regression Demand and Forecasting 8 model is much more efficient in forecasting for long term and it shows much bigger promise Therefore Dominos should establish a restaurant in our community References Curtis G E 2005 Georgia a country study Kila Mont Kessinger Publishing39s Rare Print Georgia county snapshots an overview of county demographics amp Department of Community Affairs program information 2006 Atlanta Ga Georgia Dept of Community Affairs Freedman D Pisani R amp Purves R 2001 Statistics New York Norton Draper N R amp Smith H 2004Applied regression analysis 2d ed New York Wiley
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