Stuart Symington is busy studying a marketing research report that has just arrived on his desk from the research staff. Symington is the vice president for marketing of Quantum Software Company. Quantum Software produces computer games for the young-adult market. Stuart has given the research department the task of developing a regression model to predict the likelihood of computer game purchase by young adults. To make these predictions, Symingotn and several members of the marketing research staff had examined profiles of some 300 current customers and selected the following variables (measurement indicated in parentheses): a. X 1 (number of years)
b. X 2 (female/male-dummy variables with female as the reference variable; i.e., female was coded as 0)
c. X 3 (number of years spent using personal computers)
d. X 4 (estimated annual household income)
e. X 5 (North/South -dummy variables with South as the reference variable, i.e., South was coded as 0) The research department had developed a survey to collect this information and the report of Symington displayed the regression analysis results from a sample of approximately 500 respondents. The regression model resulting from this analysis was
Likelihood of game purchase= 3.46+.67X1+.19X2+.54X3+.33X4+.40X5+E1
Based on this regression model, which independent variables have the most influence on young adults' likelihood of game purchase? How do you interpret the dummy variable of gender and geographic location? What does the number 3.46 represent in the regression model? | 
2003 McGraw-Hill Higher Education