A general reference text for MTH 522 Advanced Mathematical Statistics is “An Introduction to Statistical Learning: with Applications”.

There are two freely available versions of the book, one utilizing the R programming language, the other utilizing Python:

“An Introduction to Statistical Learning: with Applications in R“

“An Introduction to Statistical learning: with Applications in Python“

Also, see here for data files and resources mentioned throughout the text,

The authors have established a book website with extra material, including data sets and code.

See also the companion book: The Elements of Statistical Learning

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Other reference texts & videos

Statistical Inference

Zuev, K. (2016). Statistical inference. arXiv preprint arXiv:1603.04929.

Casella, G., & Berger, R. L. (2021). Statistical inference. Cengage Learning.

Effect Size

Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge university press.

Linear Regression

Applied Linear Regression, 4th Edition, S. Weisberg

Logistic Regression

Hosmer, D.W. & Lemeshow, S. (2000) Applied Logistic Regression. 2nd edition. Wiley, New York.

Understanding Logistic Regression in Python Tutorial (datacamp)

Sperandei, S. (2014) Understanding logistic regression analysis. Biochemia Medica, 24(1):12–18

Allison, P. D. (2014). Measures of fit for logistic regression. In Proceedings of the SAS global forum 2014 conference (pp. 1-13). Cary, NC, USA: SAS Institute Inc.

StatQuest: Logistic Regression (video x 7)

Python videos:

How to Implement Logistic Regression in Python From Scratch (67 minutes)

Hands-On Machine Learning: Logistic Regression with Python and Scikit-Learn.  (17 minutes)

Machine Learning Tutorial Python – 8: Logistic Regression (Binary Classification) (19 minutes)

Logistic Regression in Python from Scratch | Simply Explained

How To Implement Logistic Regression Using Python | Simplilearn. (24 minutes)

Logistic Regression – THE MATH YOU SHOULD KNOW! (9 minutes)

Time Series Analysis

Brockwell, P. J., & Davis, R. A. (Eds.). (2002). Introduction to time series and forecasting. New York, NY: Springer New York.

Cluster Analysis

Rokach, L., & Maimon, O. (2005). Clustering methods. Data mining and knowledge discovery handbook, 321-352.

Python

Python Data Science Handbook

R programming language

• The Art of R Programming
• Hands-On Programming with R
• R Packages
• Advanced R

Machine Learning

Boehmke, B., & Greenwell, B. M. (2019). Hands-on machine learning with R. Chapman and Hall/CRC.