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38.4 MB
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Sept. 17, 2025, 12:36 p.m.
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(Last updated: Sept. 17, 2025, 12:38 p.m.)
| File | Size |
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| Ismay C. Statistical Inference via Data Science. A ModernDive into R...2ed 2025.pdf | 38.4 MB |
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| Uploaded by andryold1 | Size 38.4 MB | Health [ 28 /18 ] | Added 2025-09-17 |
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SOURCE: Ismay C. Statistical Inference via Data Science. A ModernDive into R...2ed 2025
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COVER
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MEDIAINFO
Textbook in PDF format Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition offers a comprehensive guide to learning statistical inference with Data Science tools widely used in industry, academia, and government. The first part of this book introduces the Tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the infer package, a suitable complement to the Tidyverse. By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All of these tasks are performed by strongly emphasizing data visualization. Now that you’re set up with R and RStudio, you are probably asking yourself, “OK. Now how do I use R?”. The first thing to note is that unlike other statistical software programs like Excel, SPSS, or Minitab that provide point-and-click1 interfaces, R is an interpreted language. This means you have to type in commands written in R code. In other words, you have to code/program in R. Note that we’ll use the terms “coding” and “programming” interchangeably in this book. While it is not required to be a seasoned coder/computer programmer to use R, there is still a set of basic programming concepts that new R users need to understand. Consequently, while this book is not a book on programming, you will still learn just enough of these basic programming concepts needed to explore and analyze data effectively. We now introduce some basic programming concepts and terminology. Instead of asking you to memorize all these concepts and terminology right now, we’ll guide you so that you’ll “learn by doing.” To help you learn, we will always use a different font to distinguish regular text from computer_code. The best way to master these topics is, in our opinions, through deliberate practice with R and lots of repetition. Another point of confusion with many new R users is the idea of an R package. R packages extend the functionality of R by providing additional functions, data, and documentation. They are written by a worldwide community of R users and can be downloaded for free from the internet. For example, among the many packages we will use in this book are the ggplot2 package for data visualization in Chapter 2, the dplyr package. for data wrangling in Chapter 3, the moderndive package that accompanies this book, and the infer package for “tidy” and transparent statistical inference in Chapters 8, 9, and 10. A good analogy for R packages is they are like apps you can download onto a mobile phone. Preface Getting Started with Data in R Data Science with tidyverse Data Visualization Data Wrangling Data Importing and Tidy Data Statistical Modeling with moderndive Simple Linear Regression Statistical Inference with infer Sampling Estimation, Confidence Intervals, and Bootstrapping Hypothesis Testing Inference for Regression Conclusion Tell Your Story with Data
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