# Econ

## Econ

• Submitted By: classi123
• Date Submitted: 12/03/2013 9:28 AM
• Words: 1260
• Page: 6
• Views: 91

Decisions Based on Demand and Forecasting
Strayer University
ECO 550 Managerial Economics & Globalization
Professor Godfrey Ejimakor
October 28, 2013

Introduction
A market demand analysis helps company’s understand how much consumer demand there is in the industry for the product offering. Demand analysis help a brand determine the success rate of entering markets. This analysis strategy also helps determine if sufficient revenues and profits will generate enough to maintain a successful business. The following research was conducted in order to indentify market potential for expansion of The Pizza Company.
Estimated Regression Analysis
Demand analysis can serve several purposes. Demand analysis creates insight for market departments as well as future forecast in order to create effective operational decisions. It can also “project revenue which will create better, successful cash flows” (Key, 2010). Market regression analysis is often used to understand market/consumer demand for a specific location based on product offering. This type of regression analysis will also determine if a brand can successfully enter a market against competition. Moreover, regression analysis can help a “brand generate enough revenue in order to maintain market share” within the brands industry (Armstrong, 2012).
The variables used to conduct the demand regression analysis will be price, population/demand size, and median household income, competitor’s price of pizza and price of complementary products. Moreover, the demand function will look similar to QD=f(P, Ps, Pe, Y, N), where P=price of pizza, Ps=price of competitor goods, Pe=price of advertising/sale value, Y=median household income and N=population/demand size.

Regression analysis date calculations estimated by excel are as follows:
SUMMARY OUTPUT | | | | | | | | | | | | | | | | | | |Regression Statistics | | | | | | | | |Multiple R |0.912678 | | | | | | | | |R Square |0.832981 | | | | |...