This Free Ebook documents a complete logistic regression workflow, implemented across four GitHub
repositories by Roger J. DeAngelis. The series demonstrates a systematic approach to:
Too long to post, see
https://github.com/rogerjdeangelis/utl-logistic-regression-ebook-linked-to-four-github-repositories-of-reproducible-sas-compatible-code
- Optimum binning of both character and numeric covariates in preparation for logistic regression (Chapter I)
- Identifying the best five logistic models from all possible combinations of predictors (Chapter II)
- Verifying the training logistic model using a holdout sample (Chapter III)
- Extracting and presenting key outputs from the final logistic model (Chapter IV)
The methodology emphasizes interpretability, validation, and practical deployment. All code and examples are provided
in SAS compatible code, with extensive use of macros and reproducible workflows.
The repository text throughout this ebook is presented in a fixed font for clarity.
I suggest readers use the enclosed ebook to acquaint themselves with the process,
but spend more time implementing workflow using the four repositories
below.
GitHub: https://github.com/rogerjdeangelis/utl-altair-slc-chapter-I-optimum-binning-in-preparation-for-logistic-regression
GitHub: https://github.com/rogerjdeangelis/utl-altair-slc-chapter-II-identifying-the-best-five-logistic-models
GitHub: https://github.com/rogerjdeangelis/utl-altair-slc-chapter-III-verifying-training-logistic-using-holdout
GitHub: https://github.com/rogerjdeangelis/utl-altair-slc-chapter-IV-key-outputs-from-the-final-logistic-model
The ebook is formatted in Markdown and as a pdf.
