Mathematical Statistics With Applications In R

Paperback | October 30, 2017

byKandethody M. Ramachandran, Chris P. Tsokos

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Mathematical Statistics with Applications, Second Edition, gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner. The book introduces 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. Goodness of fit methods are included to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Engineering students, especially, will find these methods to be very important in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods Instructor's Manual; Solutions to Selected Problems, data sets, and image bank for students

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From the Publisher

Mathematical Statistics with Applications, Second Edition, gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner. The book introduces many modern statistical computational and simulation concepts that are not...

From the Jacket

Mathematical Statistics with Applications , Second Edition gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner.The book introduces many modern statistical computational and simulation concepts that are not ...

Chris P. Tsokos is Distinguished University Professor of Mathematics and Statistics at the University of South Florida. Dr. Tsokos research has extended into a variety of areas, including stochastic systems, statistical models, reliability analysis, ecological systems, operations research, time series, Bayesian analysis, and mathematic...

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Format:PaperbackDimensions:826 pages, 8.75 × 6.35 × 0.68 inPublished:October 30, 2017Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128100206

ISBN - 13:9780128100202

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Table of Contents

  1. Descriptive Statistics
  2. Basic Concepts from Probability Theory
  3. Additional Topics in Probability
  4. Sampling Distributions
  5. Estimation
  6. Properties of Point Estimation, Hypothesis Testing
  7. Linear Regression Models
  8. Design of Experiments
  9. Analysis of variance
  10. Bayesian Estimation and Inference
  11. Nonparametric tests
  12. Empirical Methods
  13. Time-series Analysis
  14. Overview of Statistical Applications
  15. Appendices
  16. Selected Solutions to Exercises