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Coffee Time: Multiple regression, Hypothesis testing

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A. Laura wanted to build a multiple regression model based on advertising expenditures and the company's price index. Based on the selection of all normal values she obtained the following:
1) Multiple R = 0.738
2) R-square = 0.546
By using lagged values she came up with the following:
3) Multiple R = 0.755
4) R-square = 0.570
Explain the differences in using these different models. How could her company further optimize this model?

b. Tourism is one consideration for Laura's company future. A survey of 1,233 visitors to Mumbai last year revealed that 110 visited a small cafÃ© during their visit. Laura claims that 10% of tourists will include a visit to a cafÃ©. Use a 0.05 significance level to test her claim. Would it be wise for her to use that claim in trying to convince management to increase their advertising spending to travel agents? Explain.
Finally, what additional strategy (or variation on a given strategy) would you recommend to Laura to solve the challenges given?

https://brainmass.com/statistics/regression-analysis/coffee-time-multiple-regression-hypothesis-testing-53381

Solution Summary

There are answers to questions on
1)multiple regression model (advertising expenditures and coffee times' price index) and
2) hypothesis testing
for Coffee Time.

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