Evaluation/Grading of your Final Project

Math 302 Final Project will open up Friday morning of Week 6 in the course. You have 3 full weekends to review and work on the Final Project.

Content addressed in the Final Project

In the final project, you are given a dataset and a regression output. The concept of a dataset should be something that you are familiar with because you collected one during Week 1.

There are descriptive statistics that go along with the dataset, which should also be familiar because you calculated descriptive statistics during Week 2.

The Regression output won’t look familiar to you until Week 7. Once you go through the Lessons and the Discussion Forum, (particularly your second response post) you should be familiar with how to run a Regression and what a Regression output looks like from the Data Analysis ToolPak. By the end of Week 7, you will have all the information needed to write up the Final Project. There is nothing new that you learn in Week 8 needed for the write up of the final project.

Final Project Overview

The final project is worth 100 points and no calculations are needed. You will write up an Executive Summary on what city you chose to open a second location in and justify the results. Again, no calculations are needed as they are already done for you in the attached Excel Spreadsheet. You will be writing your own Executive Summary and submitting it through Turnitin. From Turnitin, an originality report will be generated. No Turnitin report should exceed 20% of originality because you are writing this up in your own words. If any originality report is over 20%, then further action will need to be required from your instructor. This can include an automatic failure and 0 for plagiarism. If you have questions on what Academic Plagiarism is, please contact your instructor.

Grading Breakdown:

1)Executive Summary–up to 10%

1. Please review what an Executive Summary looks like: What is an Executive Summary?
2. Must have cover page.

2) Grammar–up to 10%

1. Spell and grammar check your work.
2. Make sure you have correct punctuation and complete sentences.

3) State significant predictors–up to 25%

1. Look at the Multiple Linear Regression output in Excel and state which predictors are significant at predicting Cost of Living, and provide their p-values. Use alpha = 0.05.
2. Comment on the significant predictors. What does it mean for them to be significant? Do the significant predictors make sense when considering relocation options?

4) Discuss descriptive statistics for the significant predictors–up to 25%

1. Examine the mean, median, min, max, Q1 and Q3 values for the significant predictors.
2. State which cities fall below the lower 1st quartile for each significant predictor.
3. State which cities are in the upper 3rd quartile for each significant predictor.
4. How does NYC’s values of the significant predictors compare to other cities? (Recall, the business is headquartered in NYC)

5) Recommend at least 2 cities to open a second location in–up to 30%

1. You must justify your answer for full credit, and tell us which city is your first choice and which city is your second choice.
2. You must use the Significant Predictors AND Descriptive Statistics in your justification. i. Justification without the use of Significant Predictors WILL NOT get full credit. ii. Justification without the use of Descriptive Statistics WILL NOT get full credit. You need to use both.
3. For example, let’s look back at London. London at86.75, is 13.25% less expensive than NYC. But that doesn’t necessarily imply that London is a good place to open a second location once you discuss the significant predictors and how they relate back to each city. Look at NYC and see if you can find a city that is similar. Or, you may look at NYC and look for a city that is much less expensive. Either way, justify your answer by looking at the descriptive statistics for the significant predictors.
4. Use what you have learned in the course and analyze all the data not just what you see on the surface. e. You must use the numbers and the output to justify your answers. Do not use any outside resources to justify your answer. Only use Significant Predictors AND Descriptive Statistics.