Coffee Time: Multiple regression, Hypothesis testing
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1. Laura wanted to build a multiple regression model based on advertising expenditures and price index. Based on the selection of all normal values she obtained the following: Multiple R=0.738, r2(r to the second power)=0.546
By using lagged values she came up with the following : Multiple R=0.755, r2=0.570
Explain the differences in using these different models. How could this model be further optimize this model?
2. A survey of 1,233 visitors to Mumbai last year revealead that 110 visited small coffee shop. 10% of tourist will include a visit to a cafe. Use a 0.05 significance level to test the claim. Would it be wise to use that claim in trying yo convinced management to increase their advertising spending to travel agent? Explain
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Answers to questions on multiple regression model based on advertising expenditures and coffee times' price index and hypothesis testing for Coffee Time.
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1. Laura wanted tobuild a multiple regression model based on advertising expenditures and price index. Based on the selection of all normal values she obtained the following: Multiple R=0.738, r2(r to the second power)=0.546
By using lagged values she came up with the following : Multiple R=0.755, r2=0.570
Explain the differences in using these different models. How could this model be further optimize this model?
r^2= 0.546 means that the model explains 54.60% of the variation
r^2= 0.570 means that the model explains 57.00% of the variation
So using the lagged values means that there is an improvement in the ability to predict for the model .
Using normal variables means that the explanatory variables (also called independent variables) of the same period (advertising expenditures and price index) would be used for predicting the predicted variable (also called the dependent variable) of a time period .
Using lagged variables means that the ...
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