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.

• 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