I wanted to let people know that my new book Handbook of Regression Modeling in People Analytics is now available in open review here.
Consistent with my blogging, this book is a technical guide which is intended to help those who need it to step up their knowledge of inferential modeling of data related to people. Here is an overview of its contents:
- Chapter 1 covers the context for why Regression is so important in people analytics.
- Chapter 2 covers the basics of the R programming language for those who want to attempt to jump straight in to the work in subsequent chapters but have very little R experience. Experienced R programmers can skip this chapter.
- Chapter 3 covers the bare essential statistical concepts needed to understand subsequent chapters, illustrated with real data.
- Chapter 4 covers linear regression and in the course of that introduces many other foundational concepts. The walkthrough example involves modeling academic results from prior results. The exercises involve modeling income levels based on various work and demographic factors.
- Chapter 5 covers binomial logistic regression. The walkthrough example involves modeling promotion likelihood based on performance metrics. The exercises involve modeling charitable donation likelihood based on prior donation behavior and demographics.
- Chapter 6 covers multinomial regression. The walkthrough example and exercise involves modeling the choice of three health insurance policies by company employees based on demographic and position data.
- Chapter 7 covers ordinal regression. The walkthrough example involves modeling in-game disciplinary action against soccer players based on prior discipline and other factors. The exercises involve predicting manager performance based on varied data.
- Chapter 8 covers modeling options for data with explicit or latent hierarchy. The first part covers mixed modeling and uses a model of speed dating decisions as a walkthrough and example. The second part covers structural equation modeling and uses a survey for a political party as a walkthrough example. The exercises involve modeling latent variables in an employee engagement survey.
- Chapter 9 covers survival analysis and Cox proportional hazards regression and uses employee attrition as a walkthrough example and exercise.
- Chapter 10 outlines alternative technical approaches to regression modeling in both R and Python.
I’d love to hear your feedback on this book. In particular, if you have suggested corrections or requests for additions you can log these as issues in the books Github repo by following the links on the welcome page. I’ll definitely look at all feedback, and acknowledge the contributions of anyone whose feedback I act on.