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

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
PDF
  • eBook:
    Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
  • Author:
    Chester Ismay, Albert Y. Kim
  • Edition:
    1 edition
  • Categories:
  • Data:
    December 13, 2019
  • ISBN:
    0367409828
  • ISBN-13:
    9780367409821
  • Language:
    English
  • Pages:
    460 pages
  • Format:
    PDF

Book Description
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 (dot) 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 (dot) 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.

Content

1 - Getting Started with Data in R
I. Data Science with tidyverse
2. Data Visualization
3. Data Wrangling
4. Data Importing and “Tidy” Data

II - Data Modeling with moderndive
5. Basic Regression
6. Multiple Regression

III - Statistical Inference with infer
7. Sampling
8. Bootstrapping and Confidence Intervals
9. Hypothesis Testing
10. Inference for Regression

IV - Conclusion
11. Tell Your Story with Data

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