Handbook of Regression Modeling in People Analytics : With Examples in R and Python, Second Edition

The book is a resource on inferential statistics and regression analysis and teaches how to do a range of statistical analyses in both R and Python ranging from simple hypothesis testing to advanced multivariable modelling. Although it is focused on examples related to analysis of people and talent, the methods transfer to any discipline.

Despite the recent rapid growth in machine learning, AI and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘Swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and Python, ranging from simple hypothesis testing to advanced multivariable modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to undergraduate and postgraduate students and researchers. The second edition of this book substantially expands the range of methods taught. Bayesian approaches to regression modelling are now included, as well as an in-depth chapter on causal inference theory and methods. Key Features:19 accompanying datasets across a wide range of contexts (e.g., academic, corporate, sports, marketing)Clear step-by-step instructions on executing the analysis. Clear guidance on how to interpret results. Primary instruction in R but added sections for Python coders. Discussion and data exercises for each of the main chapters. Final chapter of practice material and datasets ideal for class homework or project work.