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Concurrent model for multiple regression

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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 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 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. Laura wanted to build a multiple regression model based on advertising expenditures and coffee times 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 CoffeeTime further optimize this model?

a) The first model is concurrent model. In this we assume that the advertisement expenditure in this period affects the coffee times price index for ...

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