Regression and Machine Learning for Education Sciences Using R

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.

Table of Contents:
A brief introduction to R and R Studio Part 1: Regression models: foundation of machine learning Chapter 01: First thing first: simple regression Chapter 02: Beyond simple: multiple regression analysis Chapter 03: It takes two to tangle: regression with interactions Chapter 04: Are we thinking correctly? Checking assumptions of regression model Chapter 05: I am not straight but robust: curvilinear Robust and quantile regression Chapter 06: Predicting the class probability: logistic regression model Part 2: Machine learning: classification and predictive modeling Chapter 07: Introduction to machine learning Chapter 08. Machine learning algorithms and process Chapter 09. Let me regulate: regularized machine learning Chapter 10. Finding ways in the forest: prediction with random forest Chapter 11. I can divide better: classification with support vector machine Chapter 12. Work like a human brain: artificial neural network Chapter 13. Desire to find causal relations: bayesian network Chapter 14. We want to see the relationships: multivariate data visualization

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.