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[NEAS: The candidate writing this student project took both the regression analysis and time series courses. He asked NEAS if he could do both student projects on sports won-loss records.
The candidate’s postings on the discussion forum and his homework assignments showed the NEAS faculty that he understood the statistical concepts. NEAS replied that he should
~ Do a careful ARIMA modeling of one team for the time series student project.
~ Do an F test for the regression analysis student project.
Separate student projects are required for each on-line course. But you can use the same data, which reduces the time needed to complete the student projects.
The daily temperatures project templates can also be used for both time series and regression analysis student projects.
~ Fit an ARIMA model for the daily temperature at one location.
~ Use an F test to compare the processes for two locations.
If you use the same data for both student projects, be sure to provide complete analyses for each. For the time series student project attached to this posting, the candidate uses the Yule-Walker equations to fit an ARMA(1,1) model and to verify his AR(1) and AR(2) models, compares the implied autocorrelations for each process with the sample autocorrelations, and uses the models to forecast the last year of data.
This time series student project fits an ARIMA process to the Minnesota Twins won-loss record. The student project is a good template for your own work, for several reasons:
~ It uses data from the NEAS web site with Excel for the analysis. You can do similar analyses for any team in the four sports on the NEAS web site. If you have only Excel, want an easily accessible time series, and like sports, this is a perfect student project. More extensive data are available on other web sites, if you prefer other sports, countries, or teams, such as soccer, women’s basketball, or World Cup competition.
~ NEAS provides extensive project templates for sports won-loss records, with numerous postings and dialogues. You can review past student projects with the NEAS faculty comments to make see what is expected. Every team differs: an AR(1) model may be optimal for basketball but an AR(4) model may be better for football. Review the statistical techniques in the textbook, the NEAS postings and guides, and the past student projects with the faculty comments. Apply them to your own data.
~ This candidate explains the statistical techniques. The textbook shows the technique, but does not always provide an example. Use this student project as a model if you have trouble with a statistical technique. In particular, the candidate shows how to back into an ARMA(1,1) model and compare the implied autocorrelations with the sample autocorrelations.]