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Project: The project is your independent research and data analysis based on either primary (you collect your own) data or use some existing data. You are to design the study, collect the data (or use some existing data), analyze the data, draw appropriate conclusions, and document your study in a carefully written report.

It is important that you decide on an objective (ie. some question of interest that can be studied with a statistical investigation) and then find or generate the appropriate data to answer the question. Finding a nice clean data set and then making up a question is not recommended and will seriously impact your grade.  Use of data sets that accompany the current textbook or other books should not be used. Use of “canned” data  sets from the internet such as KAGGLE, UCI Machine Learning , etc are not recommended. Your grade will consider the originality of the data. You may lose up to 3 points (out of 25) for using one of those data sets.

Reports or papers are preferred over power-point presentations, posters or other forms of presentation. Computer files are not acceptable. See below for some guidelines on the organization of the final report.

One possible plan (but, not necessarily the only one) Start with some hypothesis, conjecture or claim. The claim might be yours or someone else's (for example: a manufacturer that claims their product lasts longer than that of a competitor). Figure out the best way to test this claim. The first step might be to figure out the correct response(s) to measure (ordinal, nominal, interval, ratio, discrete, continuous). Decide on a plan of attack, which might include doing an observational study or a designed experiment. The data to put your claim to a test might be available somewhere (on the Internet?) or you may have to collect it yourself. If you collect the data yourself, decide on an appropriate method of collection (random sample, cluster sample, etc.) and an appropriate sample size. Justify the method you used and the sample size selected. Determine the appropriate analyses to test the claim. Use of descriptive statistics is a good start but use of statistical inference (confidence intervals, hypothesis testing, etc.) should be considered a requirement. Use of more advanced analyses and specifically the techniques covered in this class should be considered. Use of elementary statistics (like t-tests, chi-square, simple regression, etc) might be an indication that your project idea lacks sufficient complexity to be considered for a high grade. Carefully complete the analysis. Draw an appropriate conclusion and carefully document what you did and your conclusions. It may also be appropriate to comment on lessons learned and potential improvements if the study was to be repeated (what you might do differently) Projects will be ranked using the criteria shown below. The instructor reserves the right to grant extra credit for a truly exceptional project (rarely done) and to give a failing grade to extremely poor projects.

Projects should be done independently. Projects involving collaboration between two or more people will NOT be accepted this semester.

Criteria for a "good" Project

· Clear statement of the problem (what is the claim? What are you trying to demonstrate?)

· Relevance of the problem to real life (why should anyone care?)

· Uniqueness/Originality

· Clear statement of conclusions (what's the bottom line? what's the decision?)

· Analysis tools used (what did you compute and why did you choose those "statistics")  \

·  Use of Graphics (if appropriate)

·  Use of Computer Aids (like MINITAB), if appropriate

·  Accuracy of calculations (No numerical mistakes)

·  Organization of Final Report.

There are no requirements on the length of the project write-up. Make it as short as possible to communicate the important steps in your analysis. Consider writing for an audience that is statistically literate but not a statistical expert. In other words, consider the audience for your report to be an average college graduate. Communicate your ideas as clearly as possible. Think Quality, NOT Quantity. Do not include any irrelevant information. Focus on the main problem. Do not include "filler" histograms, statistics, etc that are not relevant to your main thesis.   The emphasis of this course is on statistical inference or inductive logic using somewhat advanced techniques. The closer your project comes to using these ideas, the higher it will be rated. Tables and graphs, t-tests, etc are nice but try to use the ideas of the ideas presented in this class if you want your project to rate high. For some projects, the problem you choose may require you to go beyond the material covered in class in order to provide an appropriate analysis. If this is the case and you are successful, your project can rate high. However be very careful that your work is original and not following something that is available in published form elsewhere. Be sure to look at the academic honesty comments posted regarding projects.