Regression and Machine Learning for Education Sciences Using R

Cody Dingsen

• Publisher: Routledge
• Year: 2024
• Print ISBN: 9781032510071
• eBook ISBN: 9781040145067
• Edition: 1
• Topic: Psychology
• Subtopic: Social Science Research & Methodology
• Index: Psychology

This book provides a conceptual introduction to regression and machine learning and its applications in education research. The book discusses its 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 are 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 will be from the educational setting, and students will find that this text provides good preparation for studying more statistical and data analytical materials.

Table of contents

• Cover
• Half Title
• Title
• Copyright
• Contents
• List of Figures
• Preface
• A brief introduction to R and R studio
• Part 1 Regression models: foundation of machine learning
• Part 2 Machine learning: classification and predictive modeling
• Index