使用R进行教育科学的回归和机器学习
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.
英文|2024|ISBN:9781003400684|377页|True PDF，EPUB | 58.56 MB本书对回归分析和机器学习及其在教育研究中的应用进行了概念性介绍。它讨论了它们的不同应用，包括它在根据当前数据预测未来事件或解释某些现象发生的原因方面的作用。这些确定的重要预测因素为教育和心理决策提供了基于数据的证据。 本书提供了一种面向应用程序的方法，同时描绘了基本的方法论发展，为理解数据分析的基本回归和机器学习概念奠定了坚实的基础。本书的第一部分讨论了回归分析，为理解机器学习的逻辑提供了坚实的基础。每一章都从应用的角度介绍了每一个统计概念和数据分析技术的讨论和发展，统计结果为使用R的决策和问题解决方案提供了见解。这本书以实例为基础，以简洁易懂的风格写成，以学习者为中心，在分解复杂概念方面做得很好。 《使用R进行教育科学的回归和机器学习》主要面向教育和心理学领域的学生或从业者，尽管其他相关学科的个人也会发现这本书很有用。本书中使用的数据集和示例来自教育环境，学生会发现本文为学习更多的统计和数据分析材料提供了良好的准备基础。本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。
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