Book description
Understanding the world of R programming and analysis has never been easierMost guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming.
People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results.
- Gets you up to speed on the #1 analytics/data science software tool
- Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling
- Shows you how R offers intel from leading researchers in data science, free of charge
- Provides information on using R Studio to work with R
Get ready to use R to crunch and analyze your data—the fast and easy way!
Table of contents
-
- Cover
- Introduction
- Part 1: Getting Started with Statistical Analysis with R
- Part 2: Describing Data
-
Part 3: Drawing Conclusions from Data
- Chapter 9: The Confidence Game: Estimation
- Chapter 10: One-Sample Hypothesis Testing
- Chapter 11: Two-Sample Hypothesis Testing
- Chapter 12: Testing More than Two Samples
- Chapter 13: More Complicated Testing
- Chapter 14: Regression: Linear, Multiple, and the General Linear Model
- Chapter 15: Correlation: The Rise and Fall of Relationships
- Chapter 16: Curvilinear Regression: When Relationships Get Complicated
-
Part 4: Working with Probability
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Chapter 17: Introducing Probability
- What Is Probability?
- Compound Events
- Conditional Probability
- Large Sample Spaces
- R Functions for Counting Rules
- Random Variables: Discrete and Continuous
- Probability Distributions and Density Functions
- The Binomial Distribution
- The Binomial and Negative Binomial in R
- Hypothesis Testing with the Binomial Distribution
- More on Hypothesis Testing: R versus Tradition
- Chapter 18: Introducing Modeling
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Chapter 17: Introducing Probability
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Part 5: The Part of Tens
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Chapter 19: Ten Tips for Excel Emigrés
- Defining a Vector in R Is Like Naming a Range in Excel
- Operating on Vectors Is Like Operating on Named Ranges
- Sometimes Statistical Functions Work the Same Way …
- … And Sometimes They Don’t
- Contrast: Excel and R Work with Different Data Formats
- Distribution Functions Are (Somewhat) Similar
- A Data Frame Is (Something) Like a Multicolumn Named Range
- The sapply() Function Is Like Dragging
- Using edit() Is (Almost) Like Editing a Spreadsheet
- Use the Clipboard to Import a Table from Excel into R
- Chapter 20: Ten Valuable Online R Resources
-
Chapter 19: Ten Tips for Excel Emigrés
- About the Author
- Connect with Dummies
- End User License Agreement
Product information
- Title: Statistical Analysis with R For Dummies
- Author(s):
- Release date: March 2017
- Publisher(s): For Dummies
- ISBN: 9781119337065
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