Introduction
Domino pizza is a well known brand based in United States where it has a wider reach in operations. The business is the subject of this analysis where demand analysis and forecasting is used to evaluate the suitability of the business venturing in Bowie, MD. The analysis is done by collecting market data on the independent and dependent variables, calculating the regression models and the coefficient of determination whose interpretation together with the test for the regression and the variables are used as a basis of the decision. The analysis also forecasts the market demand for pizza in the region for the next four months and also uses the forecast as a basis of the decision.
Body
- Demographics and independent variables relevant for completion of a demand analysis with rationale for each.
Population
The population size of a region determines the size of the market that a business can serve. A higher population means a higher possibility of the target market being large enough to sustain businesses operations and the lower the population size, the lower the demand is expected to be. (Barron & Lynch, 1989, 52)
Prize of Pizza
Own price is the key factor in determining a product’s or good’s demand since it influences the its attractiveness to the market based on affordability which in addition to other factors defines consumers ability to purchase. A rise in own price results to a reduced demand while a reduction in the price results to increased demand. Thus, data on the price of pizza as the good in consideration in this case is necessary. (Barron & Lynch, 1989, 54)
Source: Barron & Lynch, 1989, 54.
A graphical illustration of a change in own price is shown by a shift along the current demand curve as shown above graph where pizza price rise from Pa to Pb would result to a movement along the demand curve reducing demand to Qb. (Barron & Lynch, 1989, 53)
Prize of complements including soda
Complements are usually those goods that can be used together with the good in consideration whose price also determines demand on the good. A rise in the price of a complementary good results into a fall in its demand hence consequently resulting into a fall in the demand of the good in consideration. In this case, soda is one of the key complements of pizza hence a key determinant of its demand. Therefore, data on the prize of soda in the region of consideration will be significant in analyzing demand for pizza. (Frank & Bernanke, 2001, 123)
Income
Disposable income usually defines the ability to purchase goods as consumers purchasing power is based on their ability to pay highly based on their income level. A rise in income level results to an increase in demand of a normal good like pizza while a fall in the income level results to a reduced demand for the good. In this respect, data on the income level in the region of consideration will be significant in analyzing demand for pizza. (Barron & Lynch, 1989, 54)
Source: Barron & Lynch, 1989, 54.
A change in other factors other than own price including income level and price of complimentary goods like soft drink would result to a shift of demand curve as illustrated by the graph above where the demand shift shifts from current demand curve 1 to a new demand curve 2. (Frank & Bernanke, 2001, 125)
- Data calculation and interpretation
In the calculation and presentation of the regression equation, the variables whose data is taken into consideration include price of pizza, price of soft drinks and income level providing a regression model with one dependent variable; demand and three independent variables; pizza’s price, soda’s price and income. With a current population of 54,727 in Bowie city and with the fact that 93 percent of US citizens eat pizza as well as the fact that people in US eat at least 46 pizzas per year. The data used in the calculation is as shown below. (Bowie, 2013)
Source: USA government, 2013
Regression equation
The calculation delivers a multiple linear regression model in the form of Y = a + bX1 + cX2 + dX3 where Y is demand for pizza, a, b and c are the coefficients for variables X1, X2 and X3 denoting price of pizza, price of soda and income respectively.
Demand = 424498 .87 – 5604.12 X1 – 24664.74X2 + 0.89X3
Coefficient of determination as calculated
A coefficient of determination 0.8897 as calculated is a measure of the goodness of fit of the regression equitation. The closer the value is to 1, the better the equitation fits with the data used and the dependent value. Thus, in this case, the 0.8897 indicates that there is a fit between the data used and the regression line. In addition, it means that 88.97% of the total valuation in data about the mean is explained by the fit. (Edwards, 2001)
How it influences the decision
The good fit of the regression line to the data shown by its figure of 0.8897 means that the regression line can be reliably used to determine the demand for pizza using the three variables of pizza price, soft drink price and income level. (Edwards, 2001)
How to improve the coefficient of determination
Adjusted R Squared = 1 – ((n-1)/(n-k-1)) (1-Rsquared)
Where n is the sample size and k is the number of independent variables. (Edwards, 2001)
- Test for statistical significance
Regression equation
The significance test for the regression uses the analysis of variance with the test being applied to check whether there exist a linear statistical relationship between the dependent and at least one of the independent variables.
The hypothesis being tested in this case is:
Null Ho: a = b = c = 0
Alternative Ho: a # b # b # c # # 0.
Using a significance level of 0.05, to test the hypothesis, the p value of 0.021923 which is larger than 0.05 indicates that the null hypothesis is rejected hence there is a statistical relationship between the variables. (Edwards, 2001)
Variables significance tests as calculated
The test for the significance of the coefficients considers whether there is an individual relationship between the independent variable and the dependent variable. Using the t-stat test based on the single and double tailed p values, it can then be interpreted that Pizza price, soft drinks price and income whose t values falls outside the p values range as shown on the table above range indicates that no single variable has is significant in determining the demand of pizza. (Edwards, 2001)
How it affects decision making
The significance of the regression indicates that the demand for pizzas is at least determined by one of the variables. However, the lack of significance of any variable on its own requires a combination of all the factors in determining the demand. Therefore, the entry into the market should be done in consideration of all the independent variables and not any single of them in isolation. (Edwards, 2001)
- Demand Forecast for next four months
Forecasting demand for the next four months requires data for price of pizza, price of sodas well as income. In this consideration some assumptions are taken in place to get the appropriate data for the variables as follows.
Using the demand equation Demand = 424498 .87 – 5604.12 X1 – 24664.74X2 + 0.89X3
Expected variables’ values as indicated on the table above. The forecasted demand for the next four months is as below:
Currently: 193807 Pizzas
Month 1: 193943 Pizzas
Month 2: 194078 Pizzas
Month 3: 194213 Pizzas
Month 4: 155783 Pizzas
Assumptions
- Price of pizza changes is expected to remain constant at least for three months and have a quarterly fluctuation of 1.18 5 as indicated by the US pizza market trend.
- Price of soda is expected to remain constant for about three months and change in the fourth month where the market trend indicates a fluctuation of soft drinks prices by 1.5% at least quarterly.
- Income level is assumed to increase at a monthly amount of $312 considering that the household income for Bowie has changed from $76,778 to $98,745 from the year 2000 to the year 2012. (Bowie, 2013)
- Decision
On consideration of the demand forecast for the next four months in the region, it is appropriate to conclude that the expected significant increase in demand for the first three months from the current 193807 Pizzas to first month’s 193943 Pizzas, second month’s 194078 Pizzas and third month’s 194213 Pizzas warrants the Dominio pizza to enter the market as there is expected to be a significant demand which will only be negatively affected by the expected increase in soft drinks price and pizza prizes in the fourth month where demand will reduce to 155783 Pizzas. (Edwards, 2001)
Conclusion
References
Baron, J. & Lynch, G. (1989). Economics. Boston: Richard D. Irwin Inc.
Bowie, MD. (2013). Latest news from Bowie, MD collected exclusively by city-data.com from
local newspapers, TV, and radio stations. Retrieved 09 March 2013, from http://www.city-data.com/city/Bowie-Maryland.html
CIA. (2013). World fact book: United States. Retrieved 08 March 2013, from
https://www.cia.gov/library/publications/the-world-factbook/geos/us.html
Domino’s. (2013). Domino’s Pizza. Retrieved 09 March 2013, from
http://www.dominos.com/about-pizza/
Edwards, H. (2001). Visualizing multiple regressions. Journal of statistical education. 9(1),
pp. 1-25.
Frank, B. & Bernanke, B. (2001). Principles of Microeconomics. New York: McGraw-
Hill/Irwin.
USA government. (2013). Business data and statistics. Retrieved 08 March 2013, from,
http://www.usa.gov/Business/Business-Data.shtml
