Mathematical Statistics with Applications

Other | March 13, 2009

byChris P. Tsokos, Kandethody M. Ramachandran, Kandethody M. Ramachandran

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Mathematical Statistics with Applicationsprovides a calculus-based theoretical introduction to mathematical statistics while emphasizing interdisciplinary applications as well as exposure to modern statistical computational and simulation concepts that are not covered in other textbooks. Includes the Jackknife, Bootstrap methods, the EM algorithms and Markov chain Monte Carlo methods. Prior probability or statistics knowledge is not required.

* 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
* Student Solutions Manual, Instructors Manual and data disk available

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Mathematical Statistics with Applicationsprovides a calculus-based theoretical introduction to mathematical statistics while emphasizing interdisciplinary applications as well as exposure to modern statistical computational and simulation concepts that are not covered in other textbooks. Includes the Jackknife, Bootstrap methods, the E...

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 mathemati...

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Kobo ebook|Oct 1 2014

$73.79 online$95.73list price(save 22%)
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Format:OtherDimensions:848 pages, 1 × 1 × 1 inPublished:March 13, 2009Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0080951708

ISBN - 13:9780080951706

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Table of Contents

Preface, Descriptive Statistics, Basic Concepts from Probability Theory, Additional Topics in Probability, Sampling Distributions, Point Estimation, Interval Estimation, Hypothesis Testing, Linear Regression Models, Design of Experiments, Analysis of variance, Bayesian Estimation and Inference, Nonparametric tests, Empirical Methods, Some issues in statistical applications- an overview, Appendices