Quantitative Decision-making Statistical Modeling And Analytics

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.