The attached student project uses the body mass index of Playboy centerfolds over 41 years to form an ARIMA process of society’s changing norms. The student project is excellent, both in the statistical work and the quality of the writing.
The statistical analysis in this student project uses maximum likelihood estimation. Your own student project may use the simpler techniques in the course modules to evaluate AR(1), AR(2), and MA(1) processes. (The time series shows a trend, so these processes are applied to the first differences.) This candidate mentions several similar time series that can be examined, such as waist to hip ratios, and she gives references to published references that discuss these issues.
Focus on three items as you review this student project.
Time series that reflect steady social trends are excellent for the student project. The time series itself is not stationary, but first differences create a stationary series.
Social trends are stronger some years and weaker other years. An AR(1) or AR(2) model can capture the varying force of the social trend.
Marketers (such as Playboy magazine) often vary their product to appeal to a wide audience. If a thin centerfold is chosen one month, the centerfold the next month may be less thin. An MA(1) or MA(2) model can capture this alternation.
Many social trends can be used for student projects: divorce rates, marriage ages, and crime rates are good data. Election results show both slow trends over time and alternations as the public gets tired of current politicians. You might use the percentage of Democratic vs Republican votes in House elections every two years as a time series.
The student project need not use interest rates or loss reserving. You can find hundreds of topics with enough data for a student project on public web sites. Use Google or another search engine to find web sites with data that interest you. Down load or copy the data, form a hypothesis (or take one from the web site), and test it by the methods in the regression analysis or time series course. You will gain much from the student project, and you will find it more enjoyable that you anticipate.