New
Mathematical Statistics with Applications in R,
Edition 4Editors: By Kandethody M. Ramachandran and Chris P. Tsokos
Publication Date:
01 Oct 2026
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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
About the author
By Kandethody M. Ramachandran, Professor of Mathematics and Statistics, University of South Florida (USF), ISA and Chris P. Tsokos, Distinguished University Professor of Mathematics and Statistics, University of South Florida, USA
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
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
ISBN:
9780443275487
Page Count:
730
Retail Price
:
Students in upper-level undergraduate and graduate courses in theory of statistics courses