Final Project Feedback: Hylaea's Excellent Work
Hey Hylaea!
I wanted to share some detailed feedback on your final project for EDS 222. Overall, you've done a fantastic job, and I'm really impressed with the quality of your work. Let's dive into the specifics:
Question and Data: Setting the Stage
Your blog post does an excellent job of providing context and background for your research question. The introduction is clear, engaging, and effectively draws the reader in. You've also done a superb job explaining your data. The use of multiple scatter plots is a brilliant way to visualize the relationships between your predictors and the response variable. It makes complex data immediately understandable. Furthermore, your inclusion of a Directed Acyclic Graph (DAG) to illustrate the relationships and causal pathways is spot on. This visual representation is crucial for understanding the underlying structure of your data and the potential causal links you're exploring. Great intro and clear data explanation!
Statistical Model: Clarity and Precision
When it comes to the statistical model, you've hit it out of the park. Your explanation is conceptually sound and you've also provided the formal statistical notation, which is essential for rigor. The explanation and the model formulation in statistical notation are both correct and easy to follow. I particularly liked how you demonstrated the simulation of data according to your model assumptions. This is a key step in ensuring your model is robust and reliable. Even better, you've shown that a model fit to this simulated data can successfully recover the original parameters. Presenting the simulated and estimated parameters in a table was incredibly effective and made the results easy to interpret. Nicely done on the model formulation and simulation!
Inference: Drawing Meaningful Conclusions
Your approach to inference is commendable. You've clearly stated your hypotheses in plain language, which is crucial for accessibility, and you've supported them with effective visualizations. Presenting model estimates with appropriate uncertainty, such as confidence intervals, is excellent practice and demonstrates a solid understanding of statistical inference. I especially appreciated your work showing the variation in deforestation with respect to the main driver (farming) while holding other factors constant. This kind of nuanced interpretation is highly valuable. You've successfully tested a hypothesis and provided a well-reasoned interpretation of the evidence. Great job on the hypotheses and uncertainty!
Professionalism: Polished Presentation
From a professionalism standpoint, your blog post is portfolio-quality. The figures are absolutely stunning, and your decision to hide code outputs for clarity was a smart move that keeps the focus on your findings. The writing is comprehensible to a technical audience, which is exactly what you want for this kind of project. Your code is also well-organized and appropriately documented with good commenting, making it easy to follow and reproduce. Overall, a very polished and professional presentation!
Conclusion
Seriously, Hylaea, this is outstanding work. You've demonstrated a strong grasp of the concepts, excellent data visualization skills, and a professional approach to presenting your findings. Keep up the amazing work!
For further reading on statistical modeling and data analysis, you might find these resources helpful:
- Towards Data Science: A fantastic platform with numerous articles on data science, machine learning, and statistics.
- Kaggle: Explore datasets, participate in competitions, and learn from a vibrant data science community.
- StatQuest with Josh Starmer: Excellent, clear explanations of complex statistical and machine learning concepts in an easy-to-understand format.
