Advanced Statistics with Applications in R Advanced Statistics with Applications in R
Wiley Series in Probability and Statistics

Advanced Statistics with Applications in R

    • ¥17,800
    • ¥17,800

Publisher 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. 

GENRE
Science & Nature
RELEASED
2019
November 26
LANGUAGE
EN
English
LENGTH
880
Pages
PUBLISHER
Wiley
SELLER
John Wiley & Sons, Inc.
SIZE
120.1
MB
Introduction to Bayesian Estimation and Copula Models of Dependence Introduction to Bayesian Estimation and Copula Models of Dependence
2017
Statistics for Technology Statistics for Technology
2018
NONPARAMETRIC STATISTICS: THEORY AND METHODS NONPARAMETRIC STATISTICS: THEORY AND METHODS
2017
The Probability Handbook The Probability Handbook
2016
Statistical Data Analytics Statistical Data Analytics
2015
Generalized Linear Models Generalized Linear Models
2019
M-statistics M-statistics
2023
Mixed Models Mixed Models
2013
Handbook of Regression Analysis With Applications in R Handbook of Regression Analysis With Applications in R
2020
Reinsurance Reinsurance
2017
Statistical Shape Analysis Statistical Shape Analysis
2016
Multivariate Density Estimation Multivariate Density Estimation
2015
Applied Longitudinal Analysis Applied Longitudinal Analysis
2012
Applied Linear Regression Applied Linear Regression
2013