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 of the pizza.
Demand
Price of Pizza $
Price of Soda $
Average Income Per House Hold $
Population
kids per house
10000
10
0.5
500
9655252
4
20000
10
0.5
510
9592624
4
30000
8
0.5
505
9573434
4
40000
9
0.4
505
9541108
2
50000
9
0.4
540
9555900
3
60000
8
0.4
520
9601349
5
70000
7
0.3
560
9538657
3
80000
6
0.3
580
9564907
4
90000
5
0.3
600
9656019
4
100000
6
0.3
621
9563691
5
110000
4
0.3
690
9567481
5
120000
4
0.2
650
9539521
4
130000
3
0.2
640
9571551
2
140000
2
0.5
700
9650214
4
150000
3
0.5
780
9652174
3
160000
4
0.6
750
9655122
4
170000
3
0.3
790
9676306
3
180000
2
0.2
800
9503065
4
190000
2
0.2
810
9522233
4
200000
2
0.2
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,
Variables Entered/Removed b
Model
Variables Entered
Variables Removed
Method
1
Average kids, Price of Soda, average Income, total population, Price of Pizza a
.
Enter
a. All requested variables entered.
b. Dependent Variable: D
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics
R Square Change
F Change
df1
df2
Sig. F Change
1
.988 a
.976
.967
10717.670
.976
112.985
5
14
.000
a. Predictors: (Constant), kids, PS, In, pop, PP
ANOVA b
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
6.489E10
5
1.298E10
112.985
.000 a
Residual
1.608E9
14
1.149E8
Total
6.650E10
19
a. Predictors: (Constant), kids, PS, In, pop, PP
b. Dependent Variable: D
Coefficients a
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
-25144.293
572525.126
-.044
.966
Price of Pizza
-6283.420
2385.538
-.303
-2.634
.020
Price of Soda
-32033.206
28144.482
-.069
-1.138
.274
Average Income
343.623
55.074
.675
6.239
.000
Population
-.004
.060
-.004
-.074
.942
Average kids
-806.476
2792.121
-.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 = -25144.293 - 6283.420 (Price of Pizza) - 32033.206 (Price of Soda) + 343.623 (Average Income) - 0.004 (Population) - 806.474 (Average kids)
The interpretation of each independent variable coefficient is as following,
The coefficient of price of pizza is - 6283.420 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 ...