Identify a scientific or real-world problem with a relevant data set. Translate the problem into one or two statistical questions involving hypothesis testing and regression. Make sure that the statistical questions are defined narrowly enough for you to tackle within a week. Perform exploratory data analysis and statistical analyses in R. Write a short report (< 1 page of main text), which contains the following parts:

1. Background:

a. Description of the problem;

b. Description of the statistical questions involved;

2. Results: summary of the results from data analysis in layman's terms, with figures necessary to illustrate the main points.

3. Discussion: discuss why the chosen analysis is done and what limitations there are. Discuss possible future directions: for example, the scientific/real-world problem may be explored in other ways, even though you may not know what statistical analysis to do; or additional data should be collected, etc.

4. Appendix:

a. Details of the statistical analyses; use and define math symbols where necessary.

b. R code used. The R code is expected to have adequate comments for understanding and to be run by another person without debugging.

The report is evaluated in the following aspects:

1. Is the real-world problem clearly stated?

2. Are the statistical questions clearly defined?

3. Are the statistical analyses reasonable? Are the assumptions for these analyses satisfied? If not, what considerations are given and what additional procedures are taken?

4. Do the analysis results make sense in terms of the background and in terms of statistics?

5. Are figures correctly drawn, labeled and referred to in the main text? Are they helpful for understanding the text?

6. Are the results reproducible? In other words, does the R code run without issues and generate the results described in the report?