Mathematical Statistics with Applications in R, Third Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as 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. By combining discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem-solving in a logical manner. Step-by-step procedure to solve real problems make the topics very accessible.

1. Descriptive Statistics2. Basic Concepts from Probability Theory3. Additional Topics in Probability4. Sampling Distributions5. Statistical Estimation6. Hypothesis Testing7. Linear Regression models8. Design of Experiments9. Analysis of Variance 10. Bayesian Estimation and Inference11. Categorical Data Analysis and Goodness of Fit Tests and Applications12. Nonparametric Tests13. Empirical Methods14. Some applications and Some Issues in Statistical Applications: An Overview

Subject Areas: Probability & statistics [PBT]