Resources
Library of Explantations
The internet is full of amazing explanations for all sorts of concepts. The following link is a running list of explanations that I found very insightful. Each author is tagged in the explanation and all credit belongs to the author cited. I have tried my best to credit every author for a page, but if I have missed someone: (a) I apologize and (b) let me know so I can correct that.
Explanatory Academic Papers
Below is a list of academic papers that explain some methodology or concept. The purpose of this list to collect papers that helped me understand a topic and may not contain the first paper to come up with an idea.
Disclaimer: While visualizations are a good hook for getting people interested in mathematics, they should not be used as the sole learning tool. Do read this essay that talks about the possibilities and limitations of visual explainers.
Textbooks and References
The following is a list of textbooks and references that I have found to be helpful, many of which are freely available.
Understanding linear models, ANOVA and experimental design is really important for statisticians, especially when doing statistical consulting and the most informative books in this reagard are:
- ANOVA and Mixed Models by Lukas Meier which is freely available
- Applied Linear Statistical Models, by Kutner, Nachtsheim, Neter and Li, which I think has the most complete and thorough treatment of ANOVA
Blogs
Statistics Blogs:
- Andrew Heiss
- Alex Hayes
- Dan Simpson especially this raucously funny post about gaussian processes.
- Andrew Gelman et al.
- Kat Hoffman, notably the SuperLearner explainer, and the series on TMLE
- Precision Analytics Blog especially this really great introduction to INLA by Kathryn Morrison
- Our Coding Club has fantastic tutorials on many subjects related to
Rprogramming and statistics. I personally found the mixed effects models tutorial really useful - Jesse Sadler is a historian whose blog has a number of excellent tutorials related to spatial statistics in R and tools for digitial humanities in general
- Noah Greifer is the author of many matching related packages for
R. He has a very good explanation of matching weights on his blog. - Netflix Technology Blog, especially the series on sequential A/B testing (part 1, part 2)
Visualizing Measure Theory
The level of abstraction provided by measure theory is the reason why it is a powerful and general tool for proving results in mathematics. However, this abstraction also makes it difficult to understand the concepts in measure theory. The following are a few interactive demos I made for myself to understand measure theory. They are incomplete and I have added little exposition if any. I plan to update them sometime in the future.