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Business today operates in an era defined by data, where decisions are increasingly driven by analytical insights rather than intuition alone. This book, Quantitative Decision-Making: Statistical Modeling and Analytics, has been carefully structured to provide students, researchers, and practitioners with both the theoretical foundation and the practical applications of statistics in the business world. Beginning with the fundamentals of descriptive and inferential statistics, the text progresses through advanced topics such as regression analysis, predictive modeling, big data analytics, and strategic decision-making. Each chapter integrates conceptual clarity with practical illustrations, ensuring that readers not only understand statistical principles but also learn to apply them effectively in real-world contexts. Special emphasis has been placed on bridging statistical theory with business applications—ranging from hypothesis testing in market research to predictive modeling in risk assessment and customer behavior analysis. The inclusion of tools such as Tableau, Power BI, and Python-based visualizations reflects the growing importance of software-driven analytics in modern organizations. Furthermore, topics on ethics, transparency, and responsible reporting encourage critical thinking about the role of data in shaping strategies and policies. This book aims to serve as a comprehensive guide, equipping readers with the ability to interpret data, communicate findings, and make informed decisions that foster sustainable business growth.