Week 6 Homework Overview and Tasks
Fresh Market is a small grocery chain in New Zealand. A former employee has gone to the press and told them that Fresh Market pays its female employees less than the male employees: particularly, female junior managers make less than male junior managers.
You are consulting for Fresh Market. Senior management and the head of human resources are shocked by the accusation, since they have had non-discrimination policies in place for years. They give you the data that they believe the former employee had access to, and ask you to analyze it and uncover whether there is any validity to the former employee's claims.
You decide to approach the problem in the following way:
· Obtain summary statistics about salary, for men and women separately; do the men indeed earn more than the women?
. You should find that on average, the men are paid around $3,000 more than the women; this is the claim the former employee made.
. You have racial classifications as well: White, Maori, or Pacific Islander. You should obtain summary statistics
oken down by race also.
· Build a regression model for salary using the data given:
. Salary is measured in thousands of dollars.
. Age and Experience are measured in years.
. HighSchool, College are Y (1) if the junior manager completed High School or College respectively, N (0) otherwise.
. Race and Gender are categorical variables
. DexScore is a measure of manual dexterity that forms part of an on-boarding test for the employees.
· You should find that Gender is not significant in your regression; that is, Gender is not useful for predicting salary, taking into account the other variables in the model.
. You need to reconcile these two pieces of information: men are paid more than women on average, but gender is not a significant predictor of salary.
. Hint: are men and women identical in your data, or is there some observable difference between the men and women that explains the observed difference in salary.
As always, write up your process and findings as a case report.
· Be very clear in your Executive Summary about what upper management needs to know. Does it seem that Fresh Market is discriminating, or does some other factor explain the former employee's claims.
Report Template
Executive Summary
In your executive summary, describe
iefly what you did in your homework this week and what you found. You should include a short summary of the task you were asked to complete, describe how you analyzed the data, and what you found. You should also include any conclusions
ecommendations that you generate.
The purpose of an executive summary is to explain the main findings and conclusions
ecommendations of your report/project. It should be able to be read completely apart from (and in place of, for readers who aren’t going to read) your full report.
Your executive summaries in this class should be about XXXXXXXXXXwords, depending on how much you need to summarize. Your executive summary should be the last thing you write; how could you summarize your report if you hadn’t written your report yet?
Introduction
In this class, you can keep your introduction very short. What have you been asked to do? How are you going to go about performing the tasks you need to perform? One or two paragraphs is adequate.
Data
Describe your data. What data do you have? Where does it come from? Show and discuss appropriate summary statistics and/or graphs/plots of your data. Think of this as “supporting evidence” for the analysis you perform in the next section. Try not to spend more than about a page on this.
Analysis
This is the main section of your report. Describe how you analyze your data, including a short explanation of the statistical methods you use. You may include output from Excel or Enterprise Guide here; but any output needs to be discussed in the text. (If you want to include output that you will not discuss, you can place it in the appendix.)
Try not to make this too “na
ative”, i.e. “I did this, then I did this…”. Instead, tell the story of the data: I performed statistical method X on the data in order to determine whether Y was true. The analysis shows evidence fo
against Y because…, etc.
Conclusions/Recommendations
Finally, what did you discover? What did we learn from your analysis? How can your analysis inform the decisions that will be made?