Applied Linear Statistical Models
Background
It has been over a decade since I learned R from scratch, long before Stackoverflow was there to help. With a background in statistical theory, I did not have much experience with application. Even though I was good enough at coding in R, I did not fully grasp statistical programming.
The UC Davis statistics program uses ALSM for their 106 and 108 courses on design of experiments and linear regression. This prompted me to buy the text, teach myself the content, and replicate all the examples with the included sample data using R. The results of this labor were the first iteration of the ALSM codebase, which the output can still be found at RPubs.
A Python Update
Now as a data engineer, I live and breathe Python. In late 2021 we ran into a non-linear regression problem that prompted me to run through the entire exercise again—much easier this time around. Using Jupyter notebooks makes this a lot easier because the code and results are together in the codebase and can be displayed directly from GitHub.
Lessons
While I did not go as far as the RPubs examples, I have covered the majority of the linear regression content. Chapters 5 and 13 needs to be finished and Chapter 14 has not even been started. Additional descriptions and notation could be added to the sections where no code or examples are available to really tie the book content into the notebooks. I may or may not get to the design of experiments chapters. I know it is invaluable to learn!
If any interested parties want to help maintain this codebase, feel free to submit pull requests.
Cheers
Table of Contents
Chapter 1 – Linear Regression with One Predictor Variable
Chapter 2 – Inferences in Regression and Correlation Analysis
Chapter 3 – Diagnostics and Remedial Measures
Chapter 4 – Simultaneous Inferences and Other Topics in Regression Analysis
Chapter 5 – Matrix Approach to Simple Linear Regression Analysis
Chapter 6 – Multiple Regression I
Chapter 7 – Multiple Regression II
Chapter 8 – Regression Models for Quantitative and Qualitative Predicators
Chapter 9 – Building the Regression Model I: Model Selection and Validation
Chapter 10 – Building the Regression Model II: Diagnostics
Chapter 11 – Building the Regression Model III: Remedial Measures
Chapter 12 – Autocorrelation in Time Series Data
Chapter 13 – Introduction to Nonlinear Regression and Neural Networks
Chapter 14 – Logistic Regression, Poisson Regression, and Generalized Linear Models