# multicollinearity” in the context of regression analysis and what often causes this phenomenon, not from a mathematical but data collection viewpoint?…

1a)(5 points) What is “multicollinearity” in the context of regression analysis and what often causes this phenomenon, not from a mathematical but data collection viewpoint?

1b) (5 points)Why is multicollinearity undesirable; how would you attempt to remove this phenomenon from your analysis?

2) (50 points total) Worksheet “Our Sales” in the Excel file “Our Sales Data.xls” provides information about Annual Sales (\$1,000,000) generated by a number of employees who can be considered sales representatives. Also in this worksheet are a number of employee-specific attributes, employee Aptitude Score on a standardized test, employee Age and employee Credit Score. The Aptitude Test is mandatory for all employees. Martha, the manager of the sales representatives, seeks to learn more about the relationship between the various employee attributes and Annual Sales. She directs her assistant, Derrick, to perform a regression analysis to analyze this matter (it so happens that both Marthaand Derrickare alumni of the execMBAat Missouri, so they knew about the potential benefits of a regression analysis).

Prepare each variable for the regression analysis.

2a) (5 points) Use plots to verify the nature of the relationship between sales and each of the other variables. Describe the nature of these relationships verbally and indicate how you would remedy any issues in the data. Do NOT include the plots themselves in your answer.

2b) (5 points) Determine the final (best) regression model to predict Annual Sales based on the attributes included in the data set. Show your numerical regression output (no graphs or plots), and explicitly state the regression equation. What does this regression equation tell you?

2c) (5 points) Comment on the validity and predictive power of the model you estimated in part 2c (NO need to conduct a residual analysis – base your answer strictly on the standard numerical Excel regression output).

2d) (5 points) Upper management is debating whether or not to keep Age as an independent variable. Martha is asked for her opinion. What should she recommend and what does she base her recommendation on?

2e) (5 points) Answer the same question as in part 2e, but now for the Credit Score variable.

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Regards,

Cathy, CS.