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 Presents stepbystep 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
 Includes new chapters on Categorical Data Analysis and Extreme Value Theory with Applications
 Wide array coverage of ANOVA, Nonparametric, Bayesian and empirical methods

About the Book
Mathematical Statistics with Applications in R, Third Edition, offers a modern calculusbased 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, MetropolisHastings algorithm and the Gibbs sampler. By combining discussion on the theory of statistics with a wealth of realworld applications, the book helps students to approach statistical problemsolving in a logical manner. Stepbystep procedure to solve real problems make the topics very accessible. Approx. 300 illustrations
Readership
Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course
Content
1. Descriptive Statistics 2. Basic Concepts from Probability Theory 3. Additional Topics in Probability 4. Sampling Distributions 5. Statistical Estimation 6. Hypothesis Testing 7. Linear Regression models 8. Design of Experiments 9. Analysis of Variance 10. Bayesian Estimation and Inference 11. Categorical Data Analysis and Goodness of Fit Tests and Applications 12. Nonparametric Tests 13. Empirical Methods 14. Some applications and Some Issues in Statistical Applications: An Overview




