Data Analysis
Project – Description and Instructions
This Project provides the opportunity to demonstrate skills related to some of the following course-level
Learning Outcomes:
Capacity to recognize important features of data (e.g., heterogeneity, dependence).
Understanding of zero-inflation, zero-truncation, and over/under-dispersion.
Proficiency with fitting GLMs, GAMs and their extensions.
Knowledge of hierarchical modelling frameworks and interpretation of random effects.
Understanding of tree-based methods and longitudinal models.
Appreciation for the field of spatial statistics.
Working knowledge of the R language and environment for statistical computing and graphics.
Format of Project:
1. Submit the R markdown file (RMD), datafile (.csv), and the resulting knitted .PDF file to Brightspace
under “Final Project” on or before April 6, 2023.
2. Length (excluding Figures and References) should be approximately 5000 words.
3. The following sections must be included:
a. Abstract (150 words)
b. Keywords (maximum of 10)
c. Introduction (provide necessary background and carefully explain Research Question(s))
d. Data Description (include metadata and visualizations as required)
e. Methods (state explicitly any assumptions required of your methods)
f. Analysis (logical ordering)
g. Results (model validation outcomes should be included here as well)
h. Conclusions
i. References
Grading Guidelines:
6pts. Completeness: does your submission include all required components and in the correct order?
2pts. Meeting the deadline.
2pts. Meeting the submission requirements (i.e., were all files uploaded).
5pts. Accuracy of selected visualization and inference procedures (i.e., methods) based on your
Research Question(s).
5pts. Appropriateness of formatting (e.g., are your figures easy to interpret, results interpretable?).
5pts. Accuracy of conducting the analysis in R and using R markdown (i.e., were your methods
appropriately and successfully executed in R)
5pts. Are your conclusions clear and defensible. Can they be reproduced?
Checkpoints:
1. Project Proposal: Solicit feedback from instructor and fellow students.
2. Project Presentation: Discuss and defend results and identify areas requiring clarification.