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

Chester Ismay

ISBN 10: 0367409828 / ISBN 13: 9780367409821

Published by Chapman And Hall/CRC, 2019

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering.

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Bibliographic Details

• Title: Statistical Inference via Data Science : A ModernDive into R and the Tidyverse
• Publisher: Chapman And Hall/CRC
• Publication Date: 2019
• Language: English
• ISBN 10: 0367409828
• ISBN 13: 9780367409821
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 25D millimeters 254 millimeters width by 178 millimeters height by 25 millimeters depth
• Weight: 862 grams

About this title

Synopsis

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.

About the Author

• Chester Ismay is a Data Science Evangelist for DataRobot and is based in Portland, Oregon, USA.

•Albert Y. Kim is an Assistant Professor of Statistical and Data Sciences at Smith College in Northampton, Massachusetts, USA.