Resources

I strongly encourage participants to post links to resources that they have found useful. This Google Doc is a good space to enter them, after which the workshop organizers may add them to the list on this page.

For the workshop

We won’t be using the computer much in an organized way, but you might enjoy trying out the commands used in the course.

• The easiest way, particularly for those new to R, is to follow this link to open an R session in your browser: https://posit.cloud/content/6045079

• If you already have R/RStudio installed on your laptop, you need to install the {math300} package. These two commands will do it:

install.packages("remotes")
remotes::install_github("dtkaplan/math300")

Contributed by participants

• Jeff Witmer (2023) “What should we do differently in Stat 101?” Journal of Statistics and Data Science Education, link

Materials from Danny’s course at USAFA

1. Textbook: Statistical Inference via Data Science by Chester Ismay and Albert Y. Kim
2. Textbook: Lessons in Statistical Thinking by Danny Kaplan
3. USAFA Math 300Z course outline, instructor notes, etc.
   • Classroom session videos (these are very rough, especially in the early classes when I was figuring out how to record the class).
     1. Variation and variance (See the Notes.)
     2. DAGs and simulation (See the Notes)
     3. Signal and noise
     4. Sampling variation
     5. Confidence intervals
     6. Effect size
     7. Prediction mechanics
     8. Prediction intervals
     9. Covariates
     10. Adjustment for covariates
     11. Confounding
     12. Simple causal paths
     13. Spurious correlation
     14. Experiment and random assignment
     15. Measuring & accumulating risk (NA)
     16. Constructing a classifier (NA)
     17. Accounting for prevalence
     18. Hypothesis testing (NA) .
     19. Calculating a p-value
     20. False discovery (See the Notes)

The classroom presentation of hypothesis testing is improved over the contents of Lessons 36 & 37. See the Notes for 36, 37, and 38