Regression and Machine Learning for Education Sciences Using R
Author: Cody Dingsen (Author)
Publisher finelybook 出版社: Routledge
Edition 版次: 1st
Publication Date 出版日期: 2024-11-01
Language 语言: English
Print Length 页数: 360 pages
ISBN-10: 1032510072
ISBN-13: 9781032510071
Book Description
This book provides a conceptual introduction to regression analysis and machine learning and their applications in education research. It discusses their diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur. These identified important predictors provide data-based evidence for educational and psychological decision-making.
Offering an applications-oriented approach while mapping out fundamental methodological developments, this book lays a sound foundation for understanding essential regression and machine learning concepts for data analytics. The first part of the book discusses regression analysis and provides a sturdy foundation to understand the logic of machine learning. With each chapter, the discussion and development of each statistical concept and data analytical technique is presented from an applied perspective, with the statistical results providing insights into decisions and solutions to problems using R. Based on practical examples, and written in a concise and accessible style, the book is learner-centric and does a remarkable job in breaking down complex concepts.
Regression and Machine Learning for Education Sciences Using R is primarily for students or practitioners in education and psychology, although individuals from other related disciplines can also find the book beneficial. The dataset and examples used in the book are from an educational setting, and students will find that this text provides a good preparation ground for studying more statistical and data analytical materials.
About the Author
Cody Dingsen is a professor in the Department of Educational Sciences and Professional Programs at the University of Missouri-St. Louis, Missouri, USA. His research interests include multidimensional scaling models for change and preference, psychometrics, data science, cognition and learning, emotional development, and biopsychosocial development.
