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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.
In the earlier chapters, we dedicated our time to discussing regression analysis, which serves as a fundamental concept in comprehending Machine Learning methods. This is because numerous Machine Learning methods are built upon regression-based ideas, primarily focusing on prediction. Given our knowledge of regression analysis, this chapter introduces the basic ideas of Machine Learning and discusses Machine Learning as an extension of the parametric regression models. Specifically, we discuss the concepts and relationships related to Big Data, Data Science, data analytics, data mining, and Machine Learning. The ideas of Big Data, Data Science, data analytics, data mining, and Machine Learning have been discussed for some time in the business and healthcare world, and it is time for educators to have some ideas about these concepts and to see their relevance in education. The objectives of this chapter include the following:
1. Understanding Big Data, Data Science, data analytics, data mining, and Machine Learning.
2. Understanding different types of Machine Learning algorithms.
3. Understanding the approach to validate the results from Machine Learning.
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