• Course Syllabus - course contents, rules, links, and other info
  
• Canvas - homework, solutions, grades, discussions.
  
• Main textbook (R version) - An Introduction to Statistical Learning with Applications in R by G. James, D. Witten, T. Hastie, and R. Tibshirani, 2nd edition; Springer, 2021. ISBN 1071614177.
  
• Main textbook (Python version) - An Introduction to Statistical Learning with Applications in Python by G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor; Springer, 2023. ISBN 3031387465.
  
• Book materials for the R version - Data sets, R codes, errata, slides, video lectures, etc.
  
• Book materials for the Python version - Data sets, slides, ISLP instructions, and manual.
  
• Supplementary textbook - The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by T. Hastie, R. Tibshirani, and J. Friedman, 2nd Edition; Springer, 2009. ISBN 0387848576.

R corner

Python corner

      Classroom labs

• Lab #1 First steps in R and in Python
• Lab #2 Flexibility - regression and splines in R and in Python
• Lab #3 Regression with dummy variables and interactions in R and in Python
• Lab #4 Polynomial regression in R and in Python
• Lab #5 Partial F tests and lack of fit tests in R and in Python
• Lab #6 Regression diagnostics in R and in Python
• Lab #7 KNN in R and in Python
• Lab #8 Logistic regression in R and in Python
• Lab #9 Discriminant analysis - LDA and QDA in R and in Python
• Lab #9a Geometry of LDA and QDA in R and in Python
• Lab #10 Cross-validation in R and in Python
• Lab #11 Jackknife and Bootstrap in R and in Python
• Lab #11a Bootstrap applcation to vaccine efficacy in R and in Python
• Lab #11b Jackknife and Bootstrap for Traffic Problems in R and in Python
• Lab #12 Dimension reduction - variable selection and ridge regression in R and in Python
• Lab #13 Dimension reduction - ridge regression and LASSO in R and in Python
• Lab #14 Principal components - PCR and PLS in R and in Python
• Lab #15 Polynomial regression and splines in R
• Lab #16 Trees in R and in Python
• Lab #17 Trees-2 Bagging and Random forests in R and in Python
• Lab #18 Support vector machines in R and in Python
• Lab #19 Deep Learning in R and in Python
• Lab #20 Clustering in R and in Python
• Lab #21 Applications in R

Datasets

From our textbook

Data from our textbook
You can also get all these data in R by typing
       > install.packages("ISLR2")
       > library(ISLR2)

• All data from our textbook - zip folder, RDA format
• Their descriptions
• Advertising data, CSV
• Auto data, CSV
• Boston data, CSV
• Caravan data, CSV
• Carseats data, CSV
• College data, CSV
• Default data, CSV
• Heart data, CSV
• Hitters data, CSV
• Orange Juice data, CSV
• Portfolio data, CSV
• Smarket (S&P Stock Market) data, CSV
• Wage data, CSV
• Weekly S&P Stock Market data, CSV

Other data

• US population data
• Depression data with their description
• Field Goals data and their description
• Home sales data (CSV)
• Titanic dataset and its description.
  Here is a more complete Titanic dataset and its description.
• Restaurants dataset and its description
• Birth weights dataset (birthwt) and its description from library MASS, Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Data sources for the final project

CEBM/Oxford data

Handouts

• Tables of Distributions
• P-values
• Matrices
• Regression with formulas and matrices
• Conditional probability and Bayes formula
• Bayesian Statistics
• Jackknife
• Bootstrap
• Principle of Bootstrap on Russian "Matryoshka"

Food for thought

• Bias correction, or how statisticians won WWII If the link does not open, here is the PDF file.
• BBC: Women may outsprint men by 2156 - based on this study
• Multidimensional watermelon and an article about it (right-click and hit "Translate to English")
• Clinical trial of the Pfizer COVID vaccine - results and data
• Clinical trial of the Moderna COVID vaccine - results and data

Masters Program in Statistics at American University

Study Statistics in the Capital of Data & Analysis

Main links:

• MS-Statistics program website
• Faculty and their research
• Apply to the program here

Unique opportunities:

• Accelerated "4+1" BS-to-MS program: MS degree in Statistics 1 year after BS
• MS+Cert: Graduate Certificate in Data Science integrated within the MS degree in Statistics
• Double-Eagle scholarship: 30% tuition discount
• Three super-active student clubs: meet friends and join the community

For more information:

• Email any questions to the Program Director Michael Baron
• Join my weekly Ask-Me-Anything Zoom Sessions every Monday at 12:00 pm ET. Link: https://american.zoom.us/j/95868480371
• Orientation slides: degree plans, our students, faculty, research opportunities, job opportunities, and more.

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Questions/comments/suggestions? Write to baron@american.edu