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Leveraging Quantitative Methods for Educational Leadership and Management
Abstract
This chapter explores the similarities and differences between classical statistics, mathematical analysis, and modern data science in educational leadership and management. It defines classical statistics, covering descriptive statistics, regression, hypothesis testing, time series, and clustering while emphasizing mathematical analysis as a tool for refining models, optimizing solutions, and enhancing interpretability. The chapter examines data science practices, assessing their integration into quantitative analysis. It discusses the philosophical foundations of quantitative analysis, the principles behind inference, and technological advances in data science. Finally, it evaluates data science's role in management, emphasizing AI, big data, and multidisciplinary approaches. Mathematical analysis enhances algorithm stability, predictive modeling, and decision-making, reinforcing the synergy between these fields. Their integration fosters sophisticated and effective analyses in education and management.
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