Theory of Probability and Statistics
Solved Problems and Exercises
- 1st Edition - April 1, 2027
- Latest edition
- Authors: Humbat Aliyev, Rakib F. Efendiev, Praveen Agarwal
- Language: English
Theory of Probability and Statistics: Solved Problems and Exercises is a comprehensive and practice-oriented textbook designed for undergraduate and graduate students seekin… Read more
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Description
Description
Theory of Probability and Statistics: Solved Problems and Exercises is a comprehensive and practice-oriented textbook designed for undergraduate and graduate students seeking a clear, rigorous, and intuitive understanding of probability and statistical inference. Unlike many existing titles that either lean too heavily on theory or focus mostly on computations, this book bridges the gap by integrating conceptual foundations with problem-solving techniques and real-world applications. Each chapter progresses from essential theory to fully-worked examples, followed by a rich set of exercises that reinforce learning and promote mastery.
Theory of Probability and Statistics: Solved Problems and Exercises offers significant benefits to its readers: streamlined explanations of fundamental concepts, step-by-step derivations, visual interpretations where helpful, and numerous solved problems that demonstrate how abstract principles translate into practical statistical tools. Topics span classical probability, random variables, distributions, limit theorems, estimation, hypothesis testing, regression, non-parametric methods, and modern extensions relevant to data science. This book provides a fresh, accessible approach that maintains mathematical rigor while remaining student-friendly, making it ideal for university courses, self-study, and exam preparation. By blending theory with extensive solved exercises, it fills a growing demand for a unified, application-driven text tailored to contemporary learners.
Theory of Probability and Statistics: Solved Problems and Exercises offers significant benefits to its readers: streamlined explanations of fundamental concepts, step-by-step derivations, visual interpretations where helpful, and numerous solved problems that demonstrate how abstract principles translate into practical statistical tools. Topics span classical probability, random variables, distributions, limit theorems, estimation, hypothesis testing, regression, non-parametric methods, and modern extensions relevant to data science. This book provides a fresh, accessible approach that maintains mathematical rigor while remaining student-friendly, making it ideal for university courses, self-study, and exam preparation. By blending theory with extensive solved exercises, it fills a growing demand for a unified, application-driven text tailored to contemporary learners.
Key features
Key features
- Introduces a clear and didactic understanding of essential concepts in statistics, including probability rules, conditional probability, Bayes’ Theorem, and random experiment outcomes
- Includes numerous illustrations of theoretical concepts, worked problems and examples, and step-by-step guides, which provide explanations and additional context
- Aligns with commonly offered upper-level courses in statistics, applied mathematics, and related topics in the field
- Serves as a valuable resource for students and instructors and as solid foundational material with a unified approach for early-stage researchers
- Provides support for users with ancillary content, including an Instructor's Manual, data sets, and interactive online resources
Readership
Readership
Upper-level undergraduate and graduate students majoring in statistics or related fields
Table of contents
Table of contents
1. Organization and description of data
2. Probability
3. Discrete random variables and probability distributions
4. Continuous random variables and their probability distributions
5. Sampling distributions
6. Interval estimation
7. Hypothesis testing
8. Some nonparametric tests
9. Simple linear regression Appendix and References
2. Probability
3. Discrete random variables and probability distributions
4. Continuous random variables and their probability distributions
5. Sampling distributions
6. Interval estimation
7. Hypothesis testing
8. Some nonparametric tests
9. Simple linear regression Appendix and References
Product details
Product details
- Edition: 1
- Latest edition
- Published: April 1, 2027
- Language: English
About the authors
About the authors
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Humbat Aliyev
Dr. Humbat Aliyev is an Associate Professor of Mathematics and has served both as a full-time faculty member and as a visiting professor and researcher at universities in several countries, including the Russian Federation and Kazakhstan. He has served as a visiting professor in various countries, including Malaysia and Turkey. For the past 35 years, Dr. Aliyev has been actively engaged in both research and teaching. His primary research areas include mechanics, differential equations, and probability theory. He is widely regarded as a distinguished scholar, dedicated educator, and productive researcher. Over his 30-year career in education, he authored more than ten books across a range of subjects. Dr. Aliyev is a pioneering scientist and distinguished educator who has had a profound impact through his long-standing service, steadfast commitment, and tireless dedication.
Affiliations and expertise
Professor, Department of Mathematics and Computer Science, Baku Engineering University, Baku, AzerbaijanRE
Rakib F. Efendiev
Dr. Rakib F. Efendiev is a Professor of Mathematics at Baku Engineering University with a PhD in Mathematical Analysis and Doctor of Science in Differential Equations. His research spans spectral theory, inverse problems, differential operators, and applied mathematical modeling, with publications in internationally indexed journals such as Boundary Value Problems and Partial Differential Equations in Applied Mathematics. He has participated in and organized numerous international conferences and has served as a reviewer for Mathematical Reviews and Zentralblatt MATH. Alongside his research activity, Dr. Efendiev has over three decades of teaching experience, delivering core courses in probability, statistics, calculus, and differential equations. His pedagogical focus emphasizes clarity, rigor, and problem-based learning, which directly informs the structure and methodology of this book.
Affiliations and expertise
Professor, Department of Mathematics and Computer Science, Baku Engineering University, Baku, AzerbaijanPA
Praveen Agarwal
Dr. Praveen Agarwal earned his Ph. D. in Mathematics at the Malviya National Institute of Technology (MNIT) in Jaipur, India, in 2006. Recently, Prof. Agarwal has been listed as the World's Top 2% Scientist from 2020-2025, released by Stanford University. In the 2025 ranking of best scientists worldwide announced by Research.com, he ranked 13th at the India level and 1297th worldwide in Mathematics. Dr. Agarwal has been actively involved in research as well as pedagogical activities for the last 24 years. His major research interests include Special Functions, Fractional Calculus, Numerical Analysis, Differential and Difference Equations, Inequalities, Probability & Statistics, and Fixed Point Theorems. He has published 11 research monographs and edited volumes and more than 405 publications (with almost 100 mathematicians all over the world) in prestigious national and international mathematics journals. Dr. Agarwal worked previously either as a regular faculty or as a visiting professor and scientist in universities in several countries, including India, Germany, Turkey, South Korea, UK, Russia, Malaysia and Thailand. He has served over 50 Journals in the capacity of an Editor/Honorary Editor, or Associate Editor, and published 15 books as an editor.
Affiliations and expertise
Full Professor, Anand International College of Engineering, Jaipur India; Nonlinear Dynamics Research Center (NDRC), Ajman University, Ajman, UAE; Peoples Friendship University of Russia (RUDN University), Russian Federation