Sally wanted to build a multiple regression model based on advertising expenditures and A coffee business 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 methods. How could the Coffee business further optimize this model.
Tourism is one consideration for The coffee future. A survey of 1,233 visitors to India last year revealed that 110 visited a small cafe during their visit. Sally claims that 10%of tourists will include a visit to a cafe. Use a 0.05 significance level to 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 varied) would she recommend to the key decision maker to solve the challenge. Prepare a 350 word memo to the key decision maker advocating this recommendation.
This solution explains the lagged model and reasons for the lagged adjustment and provides the null and alternative hypothesis. It uses the decision rule and calculates the test statistic to either accept or reject the null hypothesis. All steps are shown for further understanding.