Advanced Statistics with Applications in R

ISBN-10: 1118387988
ISBN-13: 9781118387986
Category: Mathematics
Pages: 880
Language: English
Published: 2019-11-12
Publisher: John Wiley & Sons
Author: Eugene Demidenko

Description

Advanced Statistics with Applications in R fills the gap between several excellent theoretical statistics textbooks and many applied statistics books where teaching reduces to using existing packages. This book looks at what is under the hood. Many statistics issues including the recent crisis with p-value are caused by misunderstanding of statistical concepts due to poor theoretical background of practitioners and applied statisticians. This book is the product of a forty-year experience in teaching of probability and statistics and their applications for solving real-life problems. There are more than 442 examples in the book: basically every probability or statistics concept is illustrated with an example accompanied with an R code. Many examples, such as Who said π? What team is better? The fall of the Roman empire, James Bond chase problem, Black Friday shopping, Free fall equation: Aristotle or Galilei, and many others are intriguing. These examples cover biostatistics, finance, physics and engineering, text and image analysis, epidemiology, spatial statistics, sociology, etc. Advanced Statistics with Applications in R teaches students to use theory for solving real-life problems through computations: there are about 500 R codes and 100 datasets. These data can be freely downloaded from the author's website dartmouth.edu/~eugened. This book is suitable as a text for senior undergraduate students with major in statistics or data science or graduate students. Many researchers who apply statistics on the regular basis find explanation of many fundamental concepts from the theoretical perspective illustrated by concrete real-world applications.

Get the book

Similar books

An Introduction to Statistical Learning: with Applications in R
By Daniela Witten, Trevor Hastie, Robert Tibshirani
This book presents some of the most important modeling and prediction techniques, along with relevant applications.
Advanced Statistical Methods in Data Science
By Hao Yu, Ding-Geng Chen, Grace Y. Yi
This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015.
Mixed Models: Theory and Applications with R
By Eugene Demidenko
... and HARRIS Fundamentals of Queueing Theory, Fourth Edition GROSS, SHORTLE, THOMPSON, and HARRIS • Solutions Manual to Accompany Fundamentals of Queueing Theory, Fourth Edition * HAHN and SHAPIRO Statistical Models in Engineering ...
Modern Industrial Statistics: with applications in R, MINITAB and JMP
By Ron S. Kenett, Shelemyahu Zacks, Daniele Amberti
Fully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-of-the-art material on both foundation and implementation tools to support their ...
R Companion to Elementary Applied Statistics
By Christopher Hay-Jahans
Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code.
Foundations and Applications of Statistics: An Introduction Using R
By Randall J. Pruim
... Andrew G. Sprau, Maurine Tong, Peggy P. White, Kurt N. Hetrick, Michael W. Barnhart, Craig W. Bark, Janet L. Goldstein, Lee Watkins, Fang Xiang, Jouko Saramies, Thomas A. Buchanan, Richard M. Watanabe, Timo T. Valle, Leena Kinnunen, ...
Applied Multivariate Statistics with R
By Daniel Zelterman
This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations.
A Course in Statistics with R
By Prabhanjan N. Tattar, Suresh Ramaiah, B. G. Manjunath
The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject.
Foundational and Applied Statistics for Biologists Using R
By Ken A. Aho
An extensive investigation of Larrea tridentata (creosote bush) distributions in the Mojave and Sonoran deserts, however, found that this convention was probably an oversimplification (Phillips and MacMahon 1981).
Learning Statistics with R
By Daniel Navarro
After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.