STAT641 Class Project (Regression Analysis, Fall, 2019)
Project Timeline:
Final draft of report is due 5pm, Thursday, December 5, 2019
Each student is expected to do a project. You can work in a group of 2 if you wish. The project should be
something pertaining to the materials we cover in the course. Each student (or group) will take some data
and apply the techniques from class. The data analysis and results will be described in a written technical
report.
Choose a Topic
First you must choose a topic to investigate. You must have one quantitative response variable and at least
two *quantitative* predictor variables.
Data
a. Decide on the response and predictor variables. Determine the appropriate model and necessary
assumptions.

b. Write a protocol for your study. How will you get your data (online research data e.g.)
Data Analysis
Using SAS (or R, Minitab, Python…),
Compute summary statistics (mean, median, standard deviation, etc.)
Produce appropriate graphs (scatterplots, histograms, boxplots...).

b. Perform a statistical analysis.
Test for overall significance of your model.
Make individual inferences about each parameter in the model.
Obtain simultaneous confidence intervals and/or prediction intervals where appropriate.

c. Check the validity of your model.
If possible, test for lack of fit.
Obtain residual plots and tests to verify your assumptions.
Check for multicollinearity, outliers, and influential observations.
Take appropriate remedial measures.
Report
Your report must be of professional quality and contain the following:
I. Explanation of Research Topic
What are you studying? Explain what you intend to show/discover with your analysis.

II. Data Collection/Data Source
Include the protocol for your study. Describe your data set and data actual collection

III. Method of Analysis
Explain the theory (model, parameters, assumptions, hypotheses, equations). Describe
how to check the validity of the model and assumptions.

IV. Results
Provide descriptive statistics and graphs. Give the results of your hypothesis tests.
Explain the significance of your findings. Was there anything usual? Did you meet the
assumptions? Were any remedial measures necessary?

V. Conclusions
Explain conclusions and interpretations in layman's terms.

VI. References
List all books, articles and web pages used.

VII. Appendices
Data set.
SAS (R or Minitab, Python…) programs and output.  Email:51zuoyejun

@gmail.com