Applied Statistics, Modeling, and Regression Analysis

Applied statistics and regression modeling form the foundation of quantitative inquiry across disciplines, enabling researchers to uncover meaningful patterns, relationships, and predictive insights from data. In the context of education and human development, these methods provide powerful tools for understanding how factors such as training, learning habits, health, and lifestyle influence academic performance and employability. At the same time, regression and statistical modeling techniques, ranging from linear and logistic regression to time-series analysis- extend these applications to broader economic and social contexts, supporting data-driven decisions in areas such as productivity, financial forecasting, and workforce development.

The studies compiled under this theme employ diverse statistical approaches, including cluster analysis, factor analysis, and multiple regression techniques, to analyze complex datasets and interpret human and institutional behaviour. Collectively, these works highlight the versatility of statistical methods in bridging theory and practice, demonstrating how quantitative analysis can inform educational improvement, social understanding, and strategic decision-making in an increasingly data-driven world.

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