Statistical Inference via Data Science - Ismay, Chester

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Résumé :
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...

Biographie:

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