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Hypothesis testing for proportion and regression model

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A. Laura wanted to build a multiple regression model based on advertising expenditures and coffee times price index. Based on the selection of all normal (i.e., concurrent) values she obtained the following:
1) Multiple R = 0.738
2) R-square = 0.546
By using lagged (time lapse between independent and dependent) 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 CoffeeTime further optimize this model?

b. Tourism is one consideration for CoffeeTime's future. A survey of 1,233 visitors to Mumbai last year revealed that 110 visited a small café during their visit. Laura claims that 10% of tourists will include a visit to a café. 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.

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Solution Summary

This solution explains how to interpret the results of regression models and to judge which model is better. It discusses the current effect and stock effect of advertising expenditure while deciding on which model is better.

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a) The first model is concurrent model. In this we assume that the adverstisement expenditure in this period affects the coffee times price index for this period. However, this may not be true. The adevrtising has two type of effects..one is current effect and other is stock effect i.e. the adevrstiosements done in previous periods will also have an impact on the coffee price inidex of this period. So, in the second model, we have ...

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