Making Decisions Based on Demand and Forecasting Research Paper



Before a company enters a new market, it is important to conduct a demand analysis and forecast for the product the company offers. Demand analysis will entail analysing the factors that are likely to affect the demand of the product at issue. Some of these factors include demographic factors (population size and pattern), household income, price of the commodity and the price of other related commodities.

It is also important to conduct competitors’ analysis in order to understand their marketing strategies, and their weaknesses and strengths. The analysis of all these factors will also make it possible for the company to assess the economic viability of the market before entering.

The purpose of research

This paper analyzes the possibility of Domino’s Pizza entering Boca Raton Market situated in Boca Raton Community. The research involves a demographic survey of the community and other variables that are likely to affect the demand of Pizza in the region.

The demand for Pizza in this community is believed to be dependent on the population size, the income per house hold, price of Pizza and the demand of soda. The demand analysis and forecast for Pizza conducted is meant to help make a decision concerning whether Domino’s should establish presence in Boca Raton community.

Research questions

  1. What is the population size of Boca Raton community and how does it affect the demand of Pizza?
  2. What is the average income per household in Boca Raton community? How does this income level influence the demand of Pizza?
  3. Does the price of pizza influence its demand in Boca Raton community?
  4. Does the price of soda have influence on the demand of pizza?


The information will be gathered from secondary sources. Journal, books and articles containing information about the price theory (demand and supply and their relationship with price) will be used. The other information to be gathered from secondary sources includes the effects of income levels and population size on demand of pizza.

The research will involve determining whether all the demand factors (price of pizza, income of households, and price of soda and population size) are important determinants of demand for pizza. The data collected will, therefore, need to be analyzed carefully and tested.

The analysis of this data will entail conducting a regression analysis in order to determine the extent to which all the independent variables affect the demand of pizza in Boca Raton, FL. The demand of pizza will be taken as the dependent variable and will be denoted by Y. The other factors will be taken as the independent variables and will be represented as follows:

X1 => Population Size.

X2 => Household income.

X3 => Price of pizza.

X4 => price of Soda.

The regression equation that will need to be determined is as follows:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4.

Where: β0, β1, β2, β3, and β4 are constants, and coefficients of the independent variables.

The estimation of the regression line will be done with the help of Eviews statistical software.

Literature review

Boca Raton, FL Demographics

Boca Raton is located in Florida, in the United States of America. The community, on average, has a population of 86,445 (United States Census Bureau, 2012). The population in the 1990’s was growing at the rate of 22% per annum on average. However, from around 2005, the population has not been growing at a very slight margin.

The community is comprised of 49% male and 515 female. The population by race comprise of 91% white, 4% Africa American, 0% Native American, 2% Asian, 0% Hawaiian and 3% for others. The population size for the past 10 years for the city is summarised in the table below.

Population by Year. Change Rate.
2000 83,014 N/A.
2001 84,251 1.49%
2002 86,005 2.08%
2003 86,034 0.03%
2004 86,597 0.65%
2005 86,325 -0.31%
2006 85,787 -0.62%
2007 85,716 -0.08%
2008 85,842 0.15%
2009 86,445 0.70%

This information was obtained from

Average income per household in Boca Raton, FL

The median household income for the community between 2006 and 2010 was $47,661 (United States Census Bureau, 2012). We shall use the following data to represent the annual household income:

Average household income by Year ($).
2000 49600
2001 52400
2002 53700
2003 60230
2004 60300
2005 47661
2006 56200
2007 50400
2008 60248
2009 49509

The price of and the demand of Pizza

According to Harvey, Carl and Hasek (2006), it is generally known that the demand for a normal commodity is inversely related to its own price. The demand of a commodity increases as its own price decreases and vice versa (McEachern, 2011).

We shall also include in the same schedule the demand for soda. Assuming that one pizza will be accompanied with one soda, the same amount of soda will be needed as pizzas. The following demand schedule for Pizza will be used in the analysis.

Demand schedule for Pizza by Year
Price of pizza. Number of Pizzas demanded Price of soda
$25 100 $13
$20 210 $11
$15 300 $9
$10 500 $7
$ 5 650 $3
30 60 $15
13 400 $8
22 160 $12
8 560 $5
27 80 $14

Data Analysis, Results and discussions.

The Eviews output was obtained as shown below:

Dependent Variable: Y.
Method: Least Squares.
Date: 10/29/12. Time: 09:03.
Sample: 1 10.
Included observations: 10.
Variable Coefficient Std. Error t-Statistic Prob.
C -1413.077 479.2690 -2.948401 0.0319
X1 0.009757 0.004869 2.003767 0.1015
X2 0.021733 0.004715 4.609647 0.0058
X3 11.11188 5.697891 1.950174 0.1086
X4 -50.38428 10.39577 -4.846612 0.0047
R-squared 0.997178 Mean dependent var 302.0000
Adjusted R-squared 0.994920 S.D. dependent var 214.4139
S.E. of regression 15.28216 Akaike info criterion 8.598101
Sum squared resid 1167.721 Schwarz criterion 8.749394
Log likelihood -37.99051 F-statistic 441.6639
Durbin-Watson stat 0.723781 Prob(F-statistic) 0.000001

The regression line is obtained from the Eviews report and is stated as follows:

Y = -1413.077 + 0.009757 X1 + 0.021733 X2 + 11.11188 X3 + -50.38428 X4

S. E. 479.2690 0.004869 0.004715 5.697891 10.39577

T – Statistic -2.948401 2.003767 4.609647 1.950174 -4.846612

To test the significance of all these independent variables, we use the t – test. We check from the student’s t distribution table for the critical values of t at 99% level of confidence and 6 (n-k) degrees of freedom. The critical t at this point is 1.9432. The hypotheses being tested are stated as follows:

H0: β = 0, that is, the independent variable (e.g. X1 x2 x3 or x4) is not an important determinant of the dependent variable Y.

H1: β ≠ 0, that is, the independent variable (e.g. X1 x2 x3 or x4) is an important determinant of the dependent variable Y.

Based on the decision criteria, we may reject or may not reject the null hypothesis. The decision criterion is that if t-statistic is greater that the critical t, we reject the null hypothesis (Black, 2009). In our case, it shows that t-statistic > t – critical, for all variables X1 x2 x3 and x4.

This means that all the 4 variables are important determinants of Y. The demand of Pizza is significantly influenced by the population size, household income, price of pizza and price of soda, which is pizza’s compliment good. The coefficient of determination R2 (Adjusted) = 0.994920. This means that

99.492% of the demand of Pizza is explained jointly by population size, household income, price of pizza, and price of soda. To improve the value of R2, other factors that affect the demand of pizza should be included in the model. These may include changes in substitute goods, the future expectations of changes in price, and tastes and preferences of the consumers among others.

Demand Forecast

Using the regression line Y = -1413.077 + 0.009757 X1 + 0.021733 X2 + 11.11188 X3 – 50.38428 X4, we assume the variables X1, X2, X3, and X4 changed as shown below in the next 4 years.

1stMonth 2ndMonth 3rdMonth 4thMonth
X1 84,251 84000 85000 83000
X2 49600 50000 51000 50500
X3 25 22 21 24
X4 13 12 16 13
Y = -1413.077 + 0.009757 X1+ 0.021733 X2 + 11.11188 X3– 50.38428 X4

The assumption made is that the regression line was estimated using monthly data.

Conclusion and recommendations

Form the above analysis, it shows that population size, household income, price of pizza, and price of soda play a big role in determining the demand of Pizza. Domino’s Pizza needs to consider all these factors before moving to the new market. The market seems to have an opportunity because the population is growing, and this will increase the demand of pizza.

The issue of household income is another important determinant. The income mostly keeps on increasing even it is with a very small margin. This means the demand of pizza will continue rising. Domino’s Pizza may also enter the market and charge slightly less than the competitors (McGuigan, Moyer and Harris, 2010). This will attract many customers toward Domino’s Pizza. The price of soda is beyond Domino’s Pizza control.

Reference List

Black, K. (2009). Business Statistics: Contemporary Decision Making. London: John Wiley & Sons.

Harvey, J., Carl, D. and Hasek, W. (2006). Economics: Principles and Applications. US: Goodwill Trading Co., Inc.

McEachern, W. A. (2011). Economics: A Contemporary Introduction. New York: Cengage Learning.

McGuigan, J. R., Moyer, C. and Harris, F. (2010). Managerial Economics. New York: Cengage Learning.

Unites States Bureau of Labor Statistics. (2001). Household survey. Web.

United States Census Bureau. (2012). State & County QuickFacts: Boca Raton (city), Florida. Web.

This research paper on Making Decisions Based on Demand and Forecasting

Leave a Comment

Your email address will not be published. Required fields are marked *