Multiple Regression Analysis: Explaining Annual Sales
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You have been assigned the task of creating a multiple regression equation of at least three variables that explains Microsoft's annual sales. Consider using a time series of data of at least 10 years. You can search for this data using the Internet.
Before running the regression, predict what sign each variable will be and explain why you make that prediction.
After running the regression, interpret the regression.
- Does the regression fit the data well?
- Does each predictor play a significant role in explaining the significance of the regression?
- Are some predictors not useful?
- If so, did you consider removing those and rerunning the regression?
- Are the predictors related too significantly to one another?
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Solution Summary
Your tutorial is 367 words plus five sources for the five variables captured. The response includes a table with five predictor variables, two regressions, a graph and a correlation matrix, which is enclosed within two attached Excel files. The discussion talks about why some variables did not show as significant in the first regression, but did in the second. The predictor variables include CPI, per capita income in US and World, number of PC users and population over 16 years old.
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Please view the attachments.
Before running the regression, predict what sign each variable will be and explain why you make that prediction.
Revenue
CPI Positive, as prices rise, so do sales
PC use (in millions) Positive, as PC use rises, so does software consumption
16 years old or older Positive, as number of potential users grows, so does sales
per capita US earnings Positive, as more disposable income, sales grow
per capita global earnings Positive, as more disposable income, sales grow
After running the regression, interpret the regression.
None of the ...
Education
- BSc, University of Virginia
- MSc, University of Virginia
- PhD, Georgia State University
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