Demographic and independent variables

Demographics of Raleigh, NC

Demographic and independent variables relevant to complete a demand analysis include population size and Average income per household. An area with a high population size is most likely to have potential for demand of products, According to the U.S. Census Bureau (2010) the there are 403,892 people in Raleigh, NC. In line with this, the population must be able to have disposable income to buy these products. The relationship between product demand and income is a good indicator of Gross Domestic Product. It is the broadest measure of income generated in the economy. However in demand analysis, personal or household incomes are useful. The income per capita of Raleigh is estimated at $29,771 (Sperlings Best Places, 2010). Therefore, it is critical for a serious business to consider these two demographic factors in demand analysis and estimation. The economy must be an active one in order for business to succeed. An active economy is one that records regular and substantial financial activities that is able to attract business.

In addition, it is expected that consumers must be willing and able to purchase products at lower prices. A business manager or an analyst must consider price as a factor in evaluation of demand. When establishing a new product or a business, it is critical for the manager to analyze prices of competitors as well as of substitute products. In this case, there are several Pizza businesses and it is critical to consider their prices to develop a comprehensive feasibility study and entry strategy. However, it is expected that an effective pricing strategy will adopt lower prices for a greater quantity in a bid to trigger demanded.

Demand Analysis

The demand analysis is done by inputting relevant data in Excel to estimate the demand for Pizza under two assumptions about what changes the quantity of Pizza demanded. Initially there will be the assumption of changes in price (PC) affecting the quantity demanded (Y) thereby using regression analysis to estimate the coefficients of the demand function.

Regression Equation

Y = a + b1X1 + b2X2 + b3X3 +b4X4


Y = Pizza demand

X1 = average price of a of pizza

X2 = Income per capita (Raleigh) 30,000 (State of North Carolina, 2012)

X3 = average price of a can of soft drink (in cents)

X4 = Area (1 Raleigh, 0 otherwise) a = constant value or Y intercept b1, b2, b3, b4 = coefficients of the X variables ‘

In this case there will be an evaluation of the statistical significance of the estimated coefficients

Coefficients; Standard Error; Confidence Intervals; T-statistic; P-value and the overall fit of the regression line R-square and F-statistic.

Therefore, according to data representation below, it appears as though there is a negative coefficient in the first variables meaning that there is an inverse relationship between the X and Pizza demand variables. However the per capita income coefficient is a positive one indicating that the people of Raleigh are able to buy Pizza if introduced. Neither the third variable, the price of soda, nor the forth variable, which is the area, do not have positive relation to the demand of Pizza. The standard error in all the four variables is below 1.0, which is an indication that there was a small variation in the estimates given different data samples. In addition, the ideal estimates of the coefficients are found within the confidence interval and analysis in this case can be 95% confident that the true value lies within that interval. In regard to the T test, the greater the value of t, specifically more than 2, the more the analyst can conclude that the estimated coefficient is significant at the 0.05 level. Therefore, it can be alluded that the results obtained from the sample are representative of the population. The P. value is an indication that there is a probability or chance that the true coefficient is actually zero. The results above show a greater P. value of the first variable and low on the other three variables. The lower the P-value for an estimated coefficient, the more confident the study is in the estimate. P-value of 0.05 or lower is commonly regarded low enough for a researcher to be confident that the estimated coefficient is statistically significant. Based on that, the estimated coefficient of variables 2, 3, and 4, are considered statistically significant. Based on the forecasting demand, Dominos should take caution in establishing a restaurant in Raleigh, NC. The data analysis does not paint an all good picture for such an investment in this area.


U.S. Census Bureau. (2010). GEO: Raleigh city, North Carolina 2010 Census. Retrieved October 22, 2012, from

Sperlings Best Places. (2010). Economy in Raleigh, North Carolina. Retrieved October 22, 2012, from

State of North Carolina . (2012). County Estimates. Retrieved October 22, 2012, from



Regression Statistics

Multiple R. 0.84649854

R Square 0.7165597783

Adjusted R. Square 0.6712093428

Standard Error 1.6404787183

Observations 30


df SS MS F. Significance F

Regression 4 170.0874060378 42.5218515095 15.8005049075 0.0000014253

Residual 25-67.2792606288 2.6911704252

Total 29-237.3

Coefficients Standard Error’t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 26.6668500385 3.2780846458 8.1348875701 0.0000000173 19.9155130617 33.4181870154 19.9155130617 33.4181870154

X Variable 1 -0.0877505051 0.0180620079 -4.8582918044 0.0000537929 -0.1249498807 -0.0505511295 -0.1249498807 -0.0505511295

X Variable 2-0.1382040915 0.0866460756 1.5950415587 0.1232684534 -0.0402467167 0.3166548997 -0.0402467167 0.3166548997

X Variable 3 -0.0758950582 0.0192256274 -3.9475985129 0.0005667496 -0.1154909513 -0.0362991651 -0.1154909513 -0.0362991651

X Variable 4 -0.5442785946 0.8846200963 -0.6152681777 0.5439383236 -2.3661865111 1.2776293218 -2.3661865111 1.2776293218


College Y X1 X2 X3 X4

1 10-100 14-100 1

2 12-100 16-95 1

3 13-90 8 110 1

4 14-95 7-90 1

5 9 110 11-100 0

6 8 125 5 100 0

7 4 125 12-125 1

8 3 150 10-150 0

9 15-80 18-100 1

10 12-80 12-90 1

11 13-90 6-80 1

12 14-100 5-75 1

13 12-100 1

14 10-110 10-125 0

15 10-125 14-130 0

16 12-110 15-80 1

17 11-150 16-90 0

18 12-100 12-95 1

19 10-150 12-100 0

20 8 150 10-90 0

21 9 150 13-95 0

22 10-125 15-100 1

23 11-125 16-95 1

24 12-100 17-100 0

25 13-75 10-100 1

26 10-100 12-110 1

27 9 110 6 125 0

28 8 125 10-90 0

29 8 150 8-80 0

30 5 150 10-95 0



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