Reference no: EM13882216
1: Poster Presentation
You will present your work in a class poster session at the end of the quarter. Your poster should highlight the problem and its motivation, your approach and principal contributions, the results of your experiments, and any major take-aways for the future. You will give a brief presentation of the highlights of your work and findings. Please practice your summary in advance.
Your poster should be no more than 48 inches high and no more than 42 inches wide (a common poster size is 30 inches high x 36 inches wide). Any poster style is acceptable.
2: Final Report
Your final project report should clearly define your problem or question of interest, review relevant past work, and introduce and detail your approach along with an interpretation of your results. If this work was done in collaboration with someone outside of the class (e.g., a professor), please describe their contributions in an acknowledgements section.
This report should contain
A. an introduction which
i. motivates the problem,
ii. states all of the main assumptions [without mathematics], and
iii. briefly summarizes your findings.
B. The main body of the report should contain
i. a detailed development of your statistical analysis, including a mathematical formulation of both the problem studied and the analytical methods employed.
ii. Use plots and graphs wherever possible to illustrate your results (preferably in R). Be sure to include with each graph an appropriate discussion and analyses. [Do not include graphs or tables that are not discussed in the text. Those can go in appendices if you used them to arrive to the final model.]
iii. All source material (including software packages used) should be cited explicitly.
iv. At the end, include a discussion of the strengths and weaknesses of the model and draw a final conclusion, including guidelines for future work.
C. A summary of your findings and conclusions in detail.
D. Appendices:Include in appendices (i) the supplementary analysis and graphs that help you construct the final model. (ii) a description of the variables in the data file submitted.
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