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, easytofollow guide that focuses on the foundational statistical concepts that R addresses—as well as stepbystep 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 cuttingedge communityreviewed 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: OneSample Hypothesis Testing
 Chapter 11: TwoSample 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
 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

Chapter 17: Introducing Probability

Part 5: The Part of Tens

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
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Product Information
 Title: Statistical Analysis with R For Dummies
 Author(s):
 Release date: March 2017
 Publisher(s): For Dummies
 ISBN: 9781119337065