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Modern Computational Statistics with R
An Introduction to Statistical Thinking, Uncertainty, and Evidence
This book is 100% completeLast updated on 2026-06-11
Modern Computational Statistics with R teaches statistics as a disciplined way of thinking: start with the scientific question, design, data, and uncertainty before reaching for formulas. Through real examples, simulations, and R, readers learn how to turn data into defensible evidence and build the statistical foundations needed for modern data science, machine learning, and AI.
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About the Book
This book is written for students who want to learn statistics as a way of thinking, not merely as a collection of formulas. Its philosophy is simple: before calculating, we must understand the question; before modelling, we must understand the design; and before interpreting results, we must understand what the data can and cannot justify. Statistical work begins with careful reasoning about populations, samples, variables, measurement, bias, uncertainty, and the logic of evidence. Formulas are important, but they are introduced as tools that serve scientific questions rather than as isolated mathematical recipes. Throughout the book, computation in R is used not to replace understanding, but to make statistical ideas visible: sampling variability can be simulated, assumptions can be checked, uncertainty can be quantified, and misleading conclusions can be exposed. The book also connects classical statistical thinking to modern data science, machine learning, and AI, where prediction, generalisation, validation, and trustworthiness all depend on the same foundations: good design, representative data, transparent assumptions, and honest communication of uncertainty. The aim is to help readers become careful statistical thinkers who can move from real-world questions to defensible evidence, from data to interpretation, and from computation to responsible decision-making.
Author
About the Author
Osama Abdelhay
Osama Abdelhay, Ph.D., is an Assistant Professor of Data Science at Princess Sumaya University for Technology in Amman, Jordan. He is a senior data scientist and biostatistician with more than 16 years of experience in statistical modelling, predictive analytics, machine learning, biostatistics, epidemiology, and humanitarian data science.
Dr. Abdelhay holds a Ph.D. in Applied Statistics and Biostatistics and a Master’s degree in Biometry from the University of Reading, United Kingdom, in addition to a Bachelor’s degree in Applied Statistics from Yarmouk University, Jordan. His academic work includes teaching undergraduate and postgraduate courses in computational statistics, data visualisation, categorical data analysis, probability, and statistics for data science. He has supervised numerous master’s theses and student projects, particularly in humanitarian, medical, and public-health contexts.
Alongside his academic role, Dr. Abdelhay has served as a consultant data scientist, biostatistician, and statistician for several international organisations, including UNICEF, UNHCR, UN-ESCWA, and Data-Pop Alliance. His work has covered female genital mutilation, child marriage, refugee vulnerability, accountability to affected populations, migration data, performance scorecards, and the use of non-traditional data sources for socio-economic estimation during crises.
His research interests include statistical modelling, interpretable machine learning, epidemiology, predictive modelling, systematic reviews, meta-analysis, and the application of data science to health and humanitarian decision-making. He has published in several peer-reviewed journals.
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