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Express the estimated demand equation in logarithms.

Q=

b. Is X a normal or an inferior good? And how are goods X and R related? Explain.

c. Which of the parameter estimates are statistically significant at the 5 percent level?

d. Estimate the own-price elasticity for good X the cross-price elasticity for goods X and R and the income elasticity for good X.

e. Holding all other things constant if household income were to fall by 22% what would we expect to happen to quantity demanded? Explain.

f. Holding all other things constant if own price were to increase by 22% what would we expect to happen to quantity demanded? Explain.

g. Holding all other things constant if the price of R were to fall by 8% what would we expect to happen to quantity demanded? Explain.

AP-8: A company wants to estimate how effective different types of advertising are for promoting its products. Specifically the company is interested in estimating the effectiveness of radio advertising and newspaper advertising. A sample of 50 cities is selected for the study during a test period of one month. The populations of the selected cities are approximately the same. Each city is allocated a specific expenditure level for radio advertising and for newspaper advertising. Sales of the product (in thousands of dollars) and the levels of expenditure (in thousands of dollars) on both types of advertising are recorded during the test month. The collected data are given in the attached Excel spreadsheet ads1.xls(see the side menu bar of this PDF).

a. Specify a suitable multiple regression equation for predicting sales.

b. Use Excel to estimate slope coeffiecients statistically significant? Explain.

c. Are the estimated slope coefficients statistically significant? Explain.

d. Interpret the estimated slope coefficients.

e. Predict the sales for a city in which radio advertising is $60000 and newspaper advertising is $60000.AP-12: The British Columbia Tourist Association distributes pamphlets maps and other tourist-related information to people who call a toll-free number and request information. The marketing manager decided to develop a multiple regression model to predict the number of calls that will be received in the coming week. A random sample of 20 weeks is selected. The collected data are summarized in the attached Excel Spreadsheet bcta1.xls (see the side menu bar of this PDF).

a. Specify a suitable multiple regression equation to estimate with the data.

b. What percentage of the total variation in the number of calls is explained by the regression model?

c. Is the overall multiple regression equation statistically significant? Explain.

d. Which if any of the independent variables is statistically significant? Test using a significance level of 0.05.