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Full 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.
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
