# Advanced Statistics with Applications in R

PDF, ePUB

- eBook:Advanced Statistics with Applications in R
- Author:Eugene Demidenko
- Edition:1 edition
- Categories:
- Data:2019-11-12
- ISBN:1118387988
- ISBN-13:9781118387986
- Language:English
- Pages:880 pages
- Format:PDF, ePUB

**Book 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.

**Content**

Chapter 2: Continuous random variables

Chapter 3: Multivariate random variables

Chapter 4: Four important distributions in statistics

Chapter 5: Preliminary data analysis and visualization

Chapter 6: Parameter estimation

Chapter 7: Hypothesis testing and confidence intervals

Chapter 8: Linear model and its extensions

Chapter 9: Nonlinear regression

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