Statistical Analysis with R For Dummies

Understanding the world of R programming and analysis has never been easier

Most 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

Part 1: Getting Started with Statistical Analysis with R
Chapter 1: Data, Statistics, and Decisions
Chapter 2: R: What It Does and How It Does It

Part 2: Describing Data
Chapter 3: Getting Graphic
Chapter 4: Finding Your Center
Chapter 5: Deviating from the Average
Chapter 6: Meeting Standards and Standings
Chapter 7: Summarizing It All
Chapter 8: What’s Normal?

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
Chapter 17: Introducing Probability
Chapter 18: Introducing Modeling

Part 5: The Part of Tens
Chapter 19: Ten Tips for Excel Emigrés
Chapter 20: Ten Valuable Online R Resources