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

Regression analysis is a powerful tool for explaining why something is happening, and this book is a comprehensive learning resource on inferential statistics and regression analysis. It teaches how to perform statistical analyses in R and Python, from simple hypothesis testing to advanced multivariate modelling, with accompanying datasets across a wide range of contexts. The book is accessible to a wide readership, from analysts and practitioners to students and researchers.

Format: Hardback
Length: 256 pages
Publication date: 30 July 2021
Publisher: Taylor & Francis Ltd

Despite the recent surge in machine learning and predictive analytics, many statistical inquiries that researchers and practitioners encounter revolve around explaining the underlying reasons behind phenomena. Regression analysis stands as a versatile and powerful tool for addressing these types of questions. This book serves as a comprehensive learning resource on inferential statistics and regression analysis. It imparts knowledge and skills to perform a diverse array of statistical analyses using both R and Python, encompassing a wide range of techniques, from basic hypothesis testing to advanced multivariate modeling. While the book primarily focuses on examples related to the analysis of people and talent, the methods showcased are easily adaptable to any discipline. The book strikes a harmonious balance between mathematical theory, providing a solid foundation for understanding the methods, and offering step-by-step guidance, easily reproducible examples, and code, enabling immediate practical application. This accessibility makes the book appealing to a broad readership, spanning public and private sector analysts and practitioners, students, and researchers alike.

Key Features:
1. Accompanying 16 comprehensive datasets spanning various contexts, including academic, corporate, sports, marketing, and more.
2. Clear and detailed step-by-step instructions for executing the analyses, ensuring a smooth learning experience.
3. Guidance on interpreting the results, aiding in making informed decisions based on the analysis.
4. Primary instruction in R, with additional sections for Python coders to facilitate understanding and implementation.
5. Engaging discussion exercises and data exercises for each main chapter, fostering a deeper understanding of the topics.
6. A final chapter dedicated to practice material and datasets, providing ample opportunities for class homework, projects, and further exploration.

Weight: 600g
Dimension: 160 x 241 x 22 (mm)
ISBN-13: 9781032041742