Mathematical Statistics with Applications in R (2nd Edition) – eBook
- Authors: Kandethody M. Ramachandran, Chris
- File Size: 36 MB
- Format: PDF
- Length: 803 pages
- Publisher: Academic Press; 2nd edition
- Publication Date: September 14, 2014
- Language: English
- ASIN: B00NV0YKZC
- ISBN-10: 0124171133
- ISBN-13: 9780124171138
Mathematical Statistics with Applications in R, 2nd Edition, (PDF) offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The ebook covers many modern statistical computational and simulation concepts that are not covered in other textbooks, such as the EM algorithms, the Jackknife, bootstrap methods, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm, and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the ebook helps college students to approach statistical problem-solving in a logical manner.
This ebook provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes the goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises, as well as practical, real-world chapter projects, are included, and each chapter has an optional section on using SPSS, Minitab, and SAS commands. The textbook also boasts a wide array of coverage of ANOVA, MCMC, nonparametric, Bayesian and empirical methods; data sets; solutions to selected problems; and an image bank for math students.
Graduate students and advanced undergraduate taking a 1 or 2-semester mathematical statistics course will find this ebook extremely useful in their studies.
- Practical, real-world chapter projects
- Exercises blend theory and modern applications
- Step-by-step procedure to solve real problems, making the topic more accessible
- Provides an optional section in each chapter on using Minitab, SPSS and SAS commands
- Wide array of coverage of ANOVA, MCMC, Nonparametric, Bayesian and empirical methods