Handbook of Regression Modeling in People Analytics: With Examples in R and Python - Softcover

McNulty, Keith

Softcover

ISBN 10: 1032046635 ISBN 13: 9781032046631

Publisher: Chapman and Hall/CRC, 2024

Synopsis

Despite the recent rapid growth in machine learning 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 in Python, ranging from simple hypothesis testing to advanced multivariate 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 students and researchers.

Key Features:

• 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing)
• Clear step-by-step instructions on executing the analyses
• Clear guidance on how to interpret results
• Primary instruction in R but added sections for Python coders
• Discussion exercises and data exercises for each of the main chapters
• Final chapter of practice material and datasets ideal for class homework or project work.

About this title

About the Author

Keith McNulty, PhD is a leading practitioner of applied statistics, psychometrics and people analytics. He is currently Global Director of Talent Science and Analytics at McKinsey and Company.

Edition details

• Publisher: Chapman and Hall/CRC
• Publication date: 2024
• Language: English
• ISBN 10: 1032046635
• ISBN 13: 9781032046631
• Binding: Paperback
• Edition number: 1
• Number of pages: 272