What properties of a car best predict its mpg? 

Statistics 183: Multiple Regression Project

The purpose of the project is for you to gain experience in applying the methods of multiple regression analysis taught in the class to a real data set of interest to you.

Due Dates

By Tuesday 10/16: Set up appointment with me or talk with me during office hours about your ideas

for the project.

By Thursday 10/18: Submit (via e-mail) paragraph describing the data set you plan to analyze and the questions of interest.

By Sunday 10/21: ( optional ) Submit (via e-mail) annotated Excel output on which your report will be based along with a few paragraphs describing your results. If you have any issues about what you should do in your data analysis, write them down for me and I will discuss them with you. I will look this over and have my comments available for you by Tuesday night. If you give me your draft earlier, I will return it to you earlier.

By Friday 10/26: Submit Final report (via Blackboard).

Project Description

The final report for the project should be a 2-3 page report (this does not include additional Excel output) that describes the questions of interest, how you used your data set to analyze these questions with details on the steps you used in your analysis, your findings about your question of interest and the limitations of your study.

Specifically, your report should contain the following:

1. Abstract: A one-paragraph summary of what you set out to learn, and what you ended up finding. It should summarize the entire report.

2. Introduction: A discussion of what questions you are interested in.

3. Data Set: Describe details about how (where) the data set was collected and the variables in the data set.

4. Analysis: Describe how you used multiple regression to analyze the data set.

Specifically, you should discuss how you carried out the steps in analysis discussed in class, i.e., exploration of data to find an initial reasonable model, checking the model and changes to the model based on your checking analysis, as well as addressed potential issues of multicollinearity.

5. Results: Provide inferences about the questions of interest and discussion.

6. Limitations and conclusion of study: Describe any limitations of your study and how they might be overcome in future research and provide brief conclusions about the results of your study.

Data Sets

The project will be of most interest to you if you find questions of interest and a data set that are of interest to you.

Examples of questions of interest are as follows:

What properties of a baseball team best predict its success over the course of a season?

What traits of a vehicle drives best predict it’s value? i.e. How does Kelly Blue Book algorithm work?

Is the unemployment rate related to economic measures such as interest rates, stock returns, and the inflation rate?

What properties of a state predict the proportion of the vote that Donald Trump (Hillary Clinton) received in it?

You will need a data set to explore your question of interest. The data set should ideally contain at least 30-50 observations (e.g., companies, people, countries, etc., as the case may be), and at least 5 variables (pieces of information about the observations; e.g., stock price, revenues, profits, salaries, gender, etc.).

One of the variables is the quantitative variable that would be of interest to try to model or forecast (e.g., for the examples above, team winning percentage, vehicle value, unemployment rate, and proportion of vote received respectively). The remaining 4 (or more) variables should contain at least 2 quantitative and 1 categorical variables .

Here are a few potential sources of data:

http://lib.stat.cmu.edu/DASL/

http://ww2.amstat.org/publications/jse/jse_data_archive.htm

http://www.lock5stat.com/datapage.html