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.
Finally, what additional strategy (or variation on a given strategy) would you recommend to the key decision maker in the simulation to solve the challenge given? Prepare a 350-word memo to the simulation's key decision maker advocating your recommendation.
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 this period. However, this may not be true. The advertising has two types of effects. One is current effect and other is stock effect i.e. the advertisements done in previous periods will also have an impact on the coffee price index of this period. So, in the second model, we have ...
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.