Identify an issue in your life (work place, home, social organization, etc.) where a statistical analysis could be used to help make a managerial decision.

Identify an issue in your life (work place, home, social organization, etc.) where a statistical analysis could be used to help make a managerial decision. Develop a sampling plan, an appropriate set of hypotheses, and an inferential statistical procedure to test them. You do not need to collect any data on this issue, but you will discuss what a significant statistical test would mean and how you would relate this result to the real-world issue you identified. Your paper should be three to five pages in length (excluding the cover and reference pages). In addition to the text, utilize at least three sources to to support your points. No abstract is required. Use the following research plan format to structure the paper:

Step 1: Identification of the problem
Describe what is known about the situation, why it is a concern, and what we do not know.

Step 2: Research Question
What exactly do we want our study to find out? This should not be phrased as a yes/no question.

Step 3: Data collection
What data is needed to answer the question, how will we collect it, and how will we decide how much we need?

Step 4: Data Analysis
Describe how you would analyze the data. Provide at least one hypothesis test (null and alternate) and an associated statistical test.

Step 5: Results and Conclusions
Describe how you would interpret the results. For example, what would you recommend if your null hypothesis was rejected and what would you do if the null was not rejected?

A quick example: Concern if gender is impacting employee s pay. H0: Gender is not related to pay. H1: Gender is related to pay. Approach: Multiple regression equation to see if gender impacts pay after considering the legal factors of grade, appraisal, education, etc. If regression coefficient for gender is significant, will need to create residual list to see which employees show excessive variation from predicted salaries when gender is not considered.