Statistical Inference via Data Science: A ModernDive into R and the Tidyverse


Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Chapman & Hall/CRC The R Series)
By 作者:Chester Ismay and Albert Y. Kim
pages 页数: 460 pages
Publisher Finelybook 出版社: Chapman and Hall/CRC; 1 edition (13 Dec. 2019)
Language 语言: English
ISBN-10 书号:0367409879
ISBN-13 书号:9780367409876
Book Description to Finelybook sorting
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout.
Features:

Assumes minimal prerequisites, notably, no prior calculus nor coding experience
Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com
Centers on simulation-based approaches to statistical inference rather than mathematical formulas
Uses the infer package for “tidy” and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods
Provides all code and output embedded directly in the text; also available in the online version at moderndive.com
This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.


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