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Project 3: Statistics

Please submit your project in class on the due date. See the Project Submission Rules.

In this project we are employing some statistical techniques for data analysis. In particular, some people may want to produce R, C++, Java, SPSS or other code/software that implements one of the methods we’ve discussed in class (e.g., EM, PCA/ICA, ANOVA, GLM). The sample data set is provided in the following table. Try to think of an interesting hypothesis or a set of interesting questions that may be addressed in some form of statistical analysis. We have deliberately not given you the detailed background on the how/why/where of the data, what we have done with it,  or what we have found (there may be expected or unexpected findings). You may work in groups (<=3) or on an individual basis. You should try to use/learn some computational tool for statistical analysis. These include, but are not limited to, SOCR, STATA, SAS, SPSS, SYSTAT, R, S-plus, or any other source (for many other examples see SOCR).

  • Format: Just as if you were actually writing this as a paper, start with a one paragraph abstract, intro/background followed by an analysis of the problem you have chosen to investigate, methods, results, discussion/conclusion and acknowledgements/references, in that order. Clearly state the problem you have chosen to investigate. List the resources you used to complete the project and reference all sources you used.
  • Clearly state your hypotheses prior to interrogating the data.
  • Use some of the statistical techniques that we have discussed or outside methods to convey whether or not there is statistical evidence in support of your original hypotheses.
  • Explicitly state the approach you used to answer your research hypotheses. Write all formulas/tests/statistics you need.
  • Interpret your statistical (numerical) results in a casual writing style. Write conclusions and discussions at the end of your report and acknowledge outside help. Describe how this project can be extended in the future.
  • We prefer electronic submissions to grace.liang@loni.usc.edu, but if you would rather turn in a paper that would be acceptable as well.