Statistical Inference via Data Science

A ModernDive into R and the Tidyverse
1. Auflage 2019
ISBN: 978-0-367-40987-6
Verlag: Chapman and Hall/CRC

Buch, Englisch, 460 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1057 g

Reihe: Chapman & Hall/CRC The R Series

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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.

Autoren/Hrsg.

Ismay, Chester

Kim, Albert Y.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Preface
1 Getting Started with Data in R
I Data Science via the tidyverse
2 Data Visualization
3 Data Wrangling
4 Data Importing & “Tidy” Data
II Data Modeling via moderndive
5 Basic Regression
6 Multiple Regression
III Statistical Inference via infer
7 Sampling
8 Bootstrapping & Confidence Intervals
9 Hypothesis Testing
10 Inference for Regression
11 Tell the Story with Data
Appendix
A Statistical Background
B Information about R packages Used
Bibliography
Index

Über Autor(innen)

• 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.