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Interpret and comparing regression models

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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.

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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 ...

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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Using the data set: Chamorro-Premuzic.sav; you will focus on the variables related to Extroversion and Agreeableness (student and lecturer)

1. Exploratory Data Analysis.

a. Perform Exploratory Data Analysis on all variables in the data set. Because you are going to focus on Extroversion and Agreeableness, be sure to include scatter plots for these combinations of variables (Student Agreeableness/Lecture Agreeableness; Student Extroversion/Lecture Extroversion; Student Agreeableness/Lecture Extroversion; Student Extroversion/Lecture Agreeableness) and include the regression line within the chart.

b. Compose a one to two paragraph write up of the data.

c. Create an APA style table that presents descriptive statistics for the sample.

2. Make a decision about the missing data. How are you going to handle it and why?

3. Correlation. Perform a correlational analysis on the following variables: Student Extroversion, Lecture Extroversion, Student Agreeableness, Lecture Agreeableness.

a. Ensure you handle missing data as you decided above.

b. State if you are using a one or two-tailed test and why.

c. Write up the results in APA style and interpret them.

4. Regression. Calculate a regression that examines whether or not you can predict if a student wants a lecturer to be extroverted using the student's extroversion score.

a. Ensure you handle missing data as you decided above.

b. State if you are using a one or two-tailed test and why.

c. Include diagnostics.

d. Discuss assumptions: are they met?

e. Write up the results in APA style and interpret them.

f. Do these results differ from the correlation results above?

5. Multiple Regression. Calculate a multiple regression that examines whether age, gender, and student's extroversion predict if a student wants the lecturer to be extroverted.

a. Ensure you handle missing data as you decided above.

b. State if you are using a one or two-tailed test and why.

c. Include diagnostics.

d. Discuss assumptions: are they met?

e. Write up the results in APA style and interpret them.

f. Do these results differ from the correlation results above?

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