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).
Class notes: MLR.
Slides by Hastie & Tibshirani: Multiple linear regression
Wikipedia: Projection matrix
Wikipedia: Leverage
Categorical predictors.
Slides by Hastie & Tibshirani: Qualitative/categorical predictors
Homework #4 – due on Friday, September 25th, 2026
Class 12: September 21st, 2026
Multiple linear
regression (cont’d) (Section
3.2.1).
R-notebook for
class: Qualitative/categorical predictors
Class notes: MLR with interactions.
R-notebook from class:
Multiple linear regression
Slides by Hastie & Tibshirani: Multiple linear regression (cont’d)
Code from class: Cherry trees
Class 13: September 23rd, 2026
Multiple linear
regression (cont’d) (Section
3.2.1).
In-Class Work #3 – skeleton
In-Class Work #3 – complete
My old class notes from Math Stats: Sample variance.
R-script from class: The
\(F-\)distribution
Slides by Hastie & Tibshirani: Multiple linear regression (cont’d)
Wikipedia: Curse of dimensionality
Wikipedia: Mallows’ \(C_p\)
Wikipedia: Akaike information criterion
Wikipedia: Bayesian information criterion
Wikipedia: Adjusted \(R^2\)
Data for class: Mustangs
Code from class: Mustangs
Homework #5 – due on Friday, October 2nd, 2026
Class 14: September 25th, 2026
Splines (Section 7.4).
Slides by Hastie & Tibshirani: Intro to Splines
Data for class: Sales
Code for class: Ads
Class 15: September 28th, 2026
More on splines
(Section
7.4).
Slides by Hastie & Tibshirani: Cubic Splines
R-notebook from class:
Splines
Code for class: Splines with validation
The Trade-Off Between Prediction Accuracy and Model Interpretability
(Section
2.1.3).
Wikipedia: Thin-plate spline
Slides by Hastie & Tibshirani: Bias-variance trade-off and interpretability
Project #2 – due on Monday, October 26th, 2026
Project #2 - this is the Rmd file which you are more than welcome to use to neatly complete your project
Class 16: September 30th, 2026
Regression vs
Classification Problems (Section
2.1.5).
Slides by Hastie & Tibshirani: Approaches to classification
K-Nearest Neighbors (Section
2.2.3, 4.7.6).
R-notebook for
class: KNN for insurance policies
In-Class Work #4 - skeleton
In-Class Work #4 - complete
Class 17: October 2nd, 2026
Logistic regression
(Sections 4.2,
4.3).
Slides by Hastie & Tibshirani: Logistic regression
Homework #6 – due on Friday, October 9th, 2026
Class 18: October 5th, 2026
More on
logistic-regression.
Wikipedia: Stock market index
R-notebook for
class: Logistic regression with the stock market
R-notebook for class:
Logistic regression with medical school
In-Class Work #5 - skeleton
In-Class Work #5 - complete
Class 19: October 7th, 2026
Logistic regression
with multiple response categories.
Slides by Hastie & Tibshirani: Logistic regression with multiple response categories
Data for class: School choice
R-notebook for class:
Multiclass logistic regression
In-Term One
In-Term One: Topics
Practice for In-Term One – problems
Practice for In-Term One – solutions
Class 20: October 9th, 2026
Collinearity (Section 3.3.3).
R-notebook for
class: Simulated collinearity
Class notes: VIF
R-notebook for class:
Variance inflation factor
Supervised vs Unsupervised Learning (Section 2.1.4).
Slides by Hastie & Tibshirani: Supervised vs. unsupervised learning
Class 21: October 12th, 2025
K-Means Clustering
(Section
12.4.1).
Slides by Hastie & Tibshirani: K-Means Clustering
Towards Data Science: K-Means Clustering
Medium: More on Initialization for K-Means Clustering
UC Irvine MLR: Data documentation with expanded data set
Data for class: Banknotes
Code for class: K-Means Clustering
Homework #7 – due on Friday, October 23rd, 2026
Class 22: October 14th, 2026
In-Term One
Class 23: October 16th, 2026
Singular value
decomposition (Section
12.2).
Slides by Hastie & Tibshirani: Introduction to PCA
Principal component analysis (PCA) (Section 12.2).
Slides by Hastie & Tibshirani: PCA
Extra-Credit Homework
YouTube: A beautiful lecture by Prof. Gilbert Strang
Extra-Credit Homework – due on Friday, , 2026
Class 24: October 19th, 2026
PCA lab.
R-notebook for class:
Principal component analysis
Data for class: Houses
R-notebook for class:
Principal component analysis for houses
In-Class Work #6 - skeleton
In-Class Work #6 - complete
Class 25: October 21st, 2026
Principal
components regression (Section
6.3.1).
Slides by Hastie & Tibshirani: PCR
R-notebook for class:
Principal component regression
R-notebook for
class: Principal component regression on seat positions
Principal components analysis and clustering (Section 12.2 and
12.4.1).
UC Irvine
MLR: Documentation for the seeds data set
R-notebook for class:
Principal component analysis with K-means
Class 26: October 23rd, 2026
Linear discriminant
analysis (Section
4.4.1).
Slides by Hastie & Tibshirani: Linear discriminant analysis for \(p=1\)
Homework #8 – due on Friday, October 30th, 2026
Class 27: October 26th, 2026
The bivariate
normal distribution.
Class 28: October 28th, 2026
The bivariate
normal distribution [practice].
Mathematica demonstration: The Bivariate Normal Distribution
Mathematica demonstration: The Bivariate Normal and Conditional Distributions
In-Class Work #7 - problems we will work on in class
In-Class Work #7 - solutions
Class 29: October 30th, 2026
Multivariate normal
distributions.
More on the linear discriminant analysis, quadratic discriminant
analysis, naive Bayes (Section
4.4.1).
Slides by Hastie & Tibshirani: More on linear discriminant analysis
Wikipedia: The Receiver Operating Characteristic (ROC)
Homework #9 – due on Friday, November 6th, 2026
Class 30: November 2nd, 2026
Even more on the
linear discriminant analysis, quadratic discriminant analysis, naive
Bayes (Section
4.4.1).
R-notebook for class: LDA,
QDA, and Naive Bayes
partimat:
Documentation
R-notebook for
class: Partition plots
Class 31: November 4th, 2026
Tree-based
regression (Section
8.1.1).
Slides by Hastie & Tibshirani: Tree-based regression
