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Statistcal Analysis of the CoffeTime Company

Quantitative and qualitative analysis of the CoffeeTime Company using regression analysis and hypothesis testing.

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A.Laura wanted to build a multiple regression model based on advertising expenditures and CoffeeTime's 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:
1)Multiple R = 0.755
2)R-square = 0.570

Explain the differences using these different models.
Regression analysis is the part of statistics that analyzes the relationship between quantitative variables. It helps to predict the reaction of a variable when a related variable varies. In this instance, Laura was attempting to determine how the dependent variable y (CoffeeTime's revenues) reacted to variations of the independent variables used in the multiple regression equation (normal and lagged figures for CoffeeTime's weekly advertising expenditure, CoffeeTime's price index, and the acquired estimate on Quick Brew's weekly advertising expenditure).

For both of the tests that Laura conducted, the coefficient of multiple determination, R2, represents the proportion of variation in the dependent variable that is explained by the independent variables.

For the first test, the calculated R2 value based on the multiple regression model is 0.546. This means that only 54% of the variation in CoffeeTime's revenues can be explained by the company's normal ...

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This file contains a formatted MS Word file containing a full statistical analysis of the CoffeeTime Company.