Making Decisions Based on Demand and Forecasting Research Paper

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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.

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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

Where:

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.....

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