MAKING DECISIONS BASED ON DEMAND AND FORECASTING

Making Decisions Based on Demand and Forecasting



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 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 ...