About these Notes

Instructor

Dr Sergey Kushnarev
Senior Lecturer, AMS JHU

About

This website is a collection of lecture notes from courses I have taught or am currently teaching.
The notes are typeset in Quarto and hosted on GitHub Pages.

Current Courses

  • Time Series Analysis (TSA)
    Lecture notes: TSA Lecture Notes
    Based on Cryer and Chen’s Time Series Analysis: With Applications in R, 2nd edition.
    Audience: Upper-level undergraduates and master’s students in Statistics/Applied Math.
    Topics: Stationarity, ARMA/ARIMA/SARIMA models, forecasting.

  • Bayesian Statistics
    Notes: Bayesian Statistics Notes Based on Peter Hoff’s A First Course in Bayesian Statistical Methods.
    Audience: Upper-level undergraduates and master’s students in Statistics/Applied Math.

    Topics: Conjugate priors, Gibbs sampling, Metropolis-Hastings sampling, model comparison, hierarchical models etc.

  • Applied Statistics and Data Analysis I
    Notes: ASDA1 To be posted Based on Kutner, Nachtsheim, Neter, and Li’s Applied Linear Statistical Models.
    Audience: Advanced undergraduates and master’s students.
    Topics: Simple and multiple linear regression, ANOVA, model selection, diagnostics, etc.

  • Applied Statistics and Data Analysis II
    Notes: ASDA2 To be posted Based on Alan Agresti’s book Foundations of Linear and Generalized Linear Models.
    Audience: Advanced undergraduates and master’s students.
    Topics: Linear regression, logistic regression, Poisson regression, GLMs.

  • Elements of Statistical Learning Notes: ESL To be posted Based on Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani’s An Introduction to Statistical Learning in R, 2nd edition. Audience: Advanced undergraduates and master’s students. Topics: Supervised/unsupervised learning, model assessment, regularization, tree-based methods, etc.

A Note on the Material

These notes are designed as teaching resources and not polished textbooks.

They include:

  • Theory, examples and proofs
  • Code snippets and exercises

Feedback is always welcome – feel free to suggest improvements or point out typos via GitHub.

License

Content is © 2025 Sergey Kushnarev unless otherwise noted. You may share or adapt the materials for non-commercial purposes with attribution.