Mathematical Statistics with Applications in R (2nd Edition)

Download Mathematical Statistics with Applications in R (2nd Edition) written by Kandethody M. Ramachandran, Chris P. Tsokos in PDF format. This book is under the category Mathematics and bearing the isbn/isbn13 number 124171133/9780124171138. You may reffer the table below for additional details of the book.


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Kandethody M. Ramachandran, Chris P. Tsokos


Academic Press; 2nd edition




803 pages









Book Description

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

Table of contents

Front Matter, Pages i-ii
Copyright, Page iv
Dedication, Page v
Acknowledgments, Page xv
About the Authors, Pages xvii-xviii
Preface to Second Edition, Pages xix-xxii
Flow Chart, Page xxiii
Chapter 1 – Descriptive Statistics, Pages 1-52
Chapter 2 – Basic Concepts from Probability Theory, Pages 53-109
Chapter 3 – Additional Topics in Probability, Pages 111-176
Chapter 4 – Sampling Distributions, Pages 177-217
Chapter 5 – Statistical Estimation, Pages 219-310
Chapter 6 – Hypothesis Testing, Pages 311-369
Chapter 7 – Goodness-of-Fit Tests Applications, Pages 371-408
Chapter 8 – Linear Regression Models, Pages 409-458
Chapter 9 – Design of Experiments, Pages 459-494
Chapter 10 – Analysis of Variance, Pages 495-547
Chapter 11 – Bayesian Estimation Inference, Pages 549-588
Chapter 12 – Nonparametric Tests, Pages 589-637
Chapter 13 – Empirical Methods, Pages 639-686
Chapter 14 – Some Issues in Statistical Applications: An Overview, Pages 687-731
Appendix A – Set Theory, Pages 733-736
Appendix B – Review of Markov Chains, Pages 737-741
Appendix C – Common Probability Distributions, Pages 743-744
Appendix D – What is R?, Page 745
Appendix E – Probability Tables, Pages 747-784
References, Pages 785-789
Index, Pages 791-800


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