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Mathematical Statistics with Applications in R

  • 4th Edition - October 1, 2026
  • Latest edition
  • Authors: Kandethody M. Ramachandran, Chris P. Tsokos
  • Language: English

Mathematical Statistics with Applications in R, Fourth Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications that spans… Read more

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Description

Mathematical Statistics with Applications in R, Fourth Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications that spans numerous foundational and essential concepts in the field. The book covers many modern statistical computational and simulation concepts, including Exploratory Data Analysis, the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. The final chapter of the book provides a step-by-step approach to modelling, analysis, and interpretation data from real-world applications, from the environment and cyber security to health and finance. By combining discussion on the theory of statistics with a wealth of engaging, real-world applications, this book helps students approach statistical problem-solving in a logical manner with accessible, step-by-step procedures on relatable topics. Computational aspects are covered through R and SAS examples.

Key features

  • Presents step-by-step procedures to solve real problems, making each topic more accessible
  • Provides updated application exercises in each chapter, blending theory and modern methods with the use of R
  • Contains practical, real-world projects and modern applications across chapters
  • Includes new chapters on exploratory data analysis and applications of statistics
  • Wide array coverage of estimation, hypothesis testing, ANOVA, nonparametric, Bayesian, empirical methods, and practical model building

Readership

Students in upper-level undergraduate and graduate courses in theory of statistics courses

Table of contents

1. Exploratory Data Analysis

2. Basic Concepts from Probability Theory

3. Distribution Functions – One Variable

4. Multivariate Distributions and Limit Theorems

5. Sampling Distributions

6. Statistical Estimation

7. Hypothesis Setting

8. Linear Regression Models

9. Design of Experiments

10. Analysis of Variance

11. Bayesian Estimation and Inference

12. Categorical Data Analysis and Goodness of Fit Tests and Applications

13. Nonparametric Statistics

14. Applications of Statistics

15. Some Real-World Applications and Modelling

Product details

  • Edition: 4
  • Latest edition
  • Published: October 1, 2026
  • Language: English

About the authors

KR

Kandethody M. Ramachandran

Kandethody M. Ramachandran is Professor of Mathematics and Statistics at the University of South Florida. His research interests are concentrated in the areas of applied probability, statistics, machine learning, and generative AI. His research publications span a variety of areas such as control of heavy traffic queues, stochastic delay systems, machine learning methods applied to game theory, finance, cyber security, health sciences, and other emerging areas. He is also co-author of three books. He is the founding director of the Interdisciplinary Data Sciences Consortium (IDSC). He is extensively involved in activities to improve statistics and mathematics education. He is a recipient of the Teaching Incentive Program award at the University of South Florida. He is also the PI of a two million dollar grant from NSF, and a co_PI of a 1.4 million grant from HHMI to improve STEM education at USF.

Affiliations and expertise
Professor of Mathematics and Statistics, University of South Florida (USF), ISA

CT

Chris P. Tsokos

Chris P. Tsokos is Distinguished University Professor of Mathematics and Statistics at the University of South Florida. Dr. Tsokos’ research has extended into a variety of areas, including stochastic systems, statistical models, reliability analysis, ecological systems, operations research, time series, Bayesian analysis, and mathematical and statistical modelling of global warming, both parametric and nonparametric survival analysis, among others. He is the author of more than 400 research publications in these areas, including Random Integral Equations with Applications to Life Sciences and Engineering, Probability Distribution: An Introduction to Probability Theory with Applications, Mainstreams of Finite Mathematics with Applications, Probability with the Essential Analysis, Applied Probability Bayesian Statistical Methods with Applications to Reliability, and Mathematical Statistics with Applications, among others. Dr. Tsokos is the recipient of many distinguished awards and honors, including Fellow of the American Statistical Association, USF Distinguished Scholar Award, Sigma Xi Outstanding Research Award, USF Outstanding Undergraduate Teaching Award, USF Professional Excellence Award, Pi Mu Epsilon, election to the International Statistical Institute, Sigma Pi Sigma, USF Teaching Incentive Program, and several humanitarian and philanthropic recognitions and awards. He is also serves as Honorary Editor, Chief-Editor, Editor or Associate Editor for more than twelve academic research journals. Dr. Tsokos is the inventor or co-inventor recipient of several U.S. patents in cybersecurity and health sciences.
Affiliations and expertise
Distinguished University Professor of Mathematics and Statistics, University of South Florida, USA