Lecture 1: Course introduction
R Markdown notebook
Lecture 2: Introduction to prediction
R Shiny app for polynomial regression
Lecture 3: Train-validate-test
Lecture 4: Cross validation
Lecture 5: Classification
CORIS dataset (adapted from Larry Wasserman)
Lecture 6: An optimization view of learning algorithms
Lecture 7: The bias-variance decomposition
The flights data used for this lecture is available on Canvas (courtesy of Bureau of Transportation, processed using the R anyflights package).
R Shiny dashboard for sampling distribution of sample mean
R Shiny dashboard with a bias-variance “tradeoff”
R Shiny dashboard with no bias-variance “tradeoff”
R Markdown notebook explaining previous dashboard
OLD Lecture 7 slides (with NBA rookies example)
Lecture 8: Introduction to frequentist statistical inference
Lecture 9: Standard errors and confidence intervals
R Shiny app for normal distribution
R Shiny app for confidence intervals for sample mean (flights data)
R Shiny app for confidence intervals for sample mean (binary data)
Lecture 10: The bootstrap
Lecture 11: Hypothesis testing
R Shiny app for p-values for binary data
R Shiny app for p-values for t statistic
R Shiny app for FPP and power for t statistic
Lecture 12: Beyond single hypothesis tests
Lecture 13: Introduction to causal inference
Lecture 14: Regression analysis of experiments
Lecture 15: Causal inference from observational data
R script for AIPW simulation
Lecture 16: Bayesian inference and decision making
R Shiny app for Bayesian posterior for binary data
R Shiny app for Bayesian posterior for normal data
Model scores (Optional)
Model selection using model scores (Optional)
F test (Optional)
Experiment design (Optional)
Instrumental variables (Optional)