Important external links
Guide to the SoA written exams
The textbook, lecture notes, and the like
Introduction to Statistical Learning - our textbook
Computer Age Statistical Inference: Algorithms, Evidence and Data Science
Elements of Statistical Learning
Mathematical
Statistics with Applications in R
A few “shortcuts” for R and RStudio
Here are some resources I created for Applied Statistics. They will be useful in this course as well and can be used in conjunction with our lecture notes.
Project Instructions – if in doubt, read and understand all of this before you work on your project
Class 1: August 24th, 2026
Orientation.
R Markdown.
Class 2: August 26th, 2026
A Tiny Review from
Mathematical Statistics.
In-Class Work #1- skeleton
In-Class Work #1 - complete
Class 3: August 28th, 2026
Confidence intervals
[review]. Resampling Methods (Sampling Distribution and Univariate
Bootstrap) (Sections 5.2 and
5.3.4).
Wikipedia: The Shapiro-Wilk test
Data set: Human temperatures
R-notebook:
Temperature bootstrap
Data set: Chemicals in Bangladesh
R-notebook from
class: Arsenic (prompts only)
R-notebook from
class: Arsenic (complete)
Homework #1 – due on Friday, September 4th, 2026
Class 4: August 31st, 2026
Resampling Methods
(Bootstrap, cont’d) (Sections 5.2
and 5.3.4).
Slides by Hastie & Tibshirani: Univariate bootstrap
R-notebook from
class: Two-stock portfolio
PROJECT #1
Project #1 – due on Monday, September 21st, 2026
Project #1 - this is the Rmd file which you are more than welcome to use to neatly complete your project
Class 5: September 2nd, 2026
Prediction and
inference (Section
2.1.1).
Slides by Hastie & Tibshirani: Prediction
Class 6: September 4th, 2026
More on prediction
and inference (Section
2.1.1).
Homework #2 – due on Friday, September 11th, 2026
Class 7: September 9th, 2026
Simple Linear
Regression. (Section
3.1).
Slides by Hastie & Tibshirani: Simple linear regression
R-script for class:
Simple linear regression
Class 8: September 11th, 2026
More on Resampling
Methods (Sampling Distribution of the Slope Coefficient) (Section 5.3.4).
R-notebook from class:
Bootstrap for simple linear regression
R-script from class:
Polynomial fit
Homework #3 – due on Friday, September 18th, 2026
Class 9: September 14th, 2026
Simple Linear
Regression: Cross Validation (Section 5.1.1, 5.1.2, 5.1.3, 5.3.1,
5.3.2, 5.3.3).
Slides by Hastie & Tibshirani: Cross-validation
R-notebook from
class: Cross-validation for simple linear regression
Class 10: September 16th, 2026
Lines, planes,
hyperplanes.
Class notes: Lines. Hyperplanes.
In-Class Work #2 – skeleton
In-Class Work #2 – complete
Class 11: September 18th, 2026
Multiple linear
regression (Section
3.2.1).
Slides by Hastie & Tibshirani: Multiple linear regression
Wikipedia: Projection matrix
Wikipedia: Leverage
