Statistical Analysis with R For Dummies®

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1. Table of Contents
   1. Cover
   2. Introduction
      1. About This Book
      2. Similarity with This Other For Dummies Book
      3. What You Can Safely Skip
      4. Foolish Assumptions
      5. How This Book Is Organized
      6. Icons Used in This Book
      7. Where to Go from Here
   3. Part 1: Getting Started with Statistical Analysis with R
      1. Chapter 1: Data, Statistics, and Decisions
         1. The Statistical (and Related) Notions You Just Have to Know
         2. Inferential Statistics: Testing Hypotheses
      2. Chapter 2: R: What It Does and How It Does It
         1. Downloading R and RStudio
         2. A Session with R
         3. R Functions
         4. User-Defined Functions
         5. Comments
         6. R Structures
         7. Packages
         8. More Packages
         9. R Formulas
         10. Reading and Writing
   4. Part 2: Describing Data
      1. Chapter 3: Getting Graphic
         1. Finding Patterns
         2. Base R Graphics
         3. Graduating to ggplot2
         4. Wrapping Up
      2. Chapter 4: Finding Your Center
         1. Means: The Lure of Averages
         2. The Average in R: mean()
         3. Medians: Caught in the Middle
         4. The Median in R: median()
         5. Statistics à la Mode
         6. The Mode in R
      3. Chapter 5: Deviating from the Average
         1. Measuring Variation
         2. Back to the Roots: Standard Deviation
         3. Standard Deviation in R
         4. Conditions, Conditions, Conditions …
      4. Chapter 6: Meeting Standards and Standings
         1. Catching Some Z’s
         2. Standard Scores in R
         3. Where Do You Stand?
         4. Summarizing
      5. Chapter 7: Summarizing It All
         1. How Many?
         2. The High and the Low
         3. Living in the Moments
         4. Tuning in the Frequency
         5. Summarizing a Data Frame
      6. Chapter 8: What’s Normal?
         1. Hitting the Curve
         2. Working with Normal Distributions
         3. A Distinguished Member of the Family
   5. Part 3: Drawing Conclusions from Data
      1. Chapter 9: The Confidence Game: Estimation
         1. Understanding Sampling Distributions
         2. An EXTREMELY Important Idea: The Central Limit Theorem
         3. Confidence: It Has Its Limits!
         4. Fit to a t
      2. Chapter 10: One-Sample Hypothesis Testing
         1. Hypotheses, Tests, and Errors
         2. Hypothesis Tests and Sampling Distributions
         3. Catching Some Z’s Again
         4. Z Testing in R
         5. t for One
         6. t Testing in R
         7. Working with t-Distributions
         8. Visualizing t-Distributions
         9. Testing a Variance
         10. Working with Chi-Square Distributions
         11. Visualizing Chi-Square Distributions
      3. Chapter 11: Two-Sample Hypothesis Testing
         1. Hypotheses Built for Two
         2. Sampling Distributions Revisited
         3. t for Two
         4. Like Peas in a Pod: Equal Variances
         5. t-Testing in R
         6. A Matched Set: Hypothesis Testing for Paired Samples
         7. Paired Sample t-testing in R
         8. Testing Two Variances
         9. Working with F-Distributions
         10. Visualizing F-Distributions
      4. Chapter 12: Testing More than Two Samples
         1. Testing More Than Two
         2. ANOVA in R
         3. Another Kind of Hypothesis, Another Kind of Test
         4. Getting Trendy
         5. Trend Analysis in R
      5. Chapter 13: More Complicated Testing
         1. Cracking the Combinations
         2. Two-Way ANOVA in R
         3. Two Kinds of Variables … at Once
         4. After the Analysis
         5. Multivariate Analysis of Variance
      6. Chapter 14: Regression: Linear, Multiple, and the General Linear Model
         1. The Plot of Scatter
         2. Graphing Lines
         3. Regression: What a Line!
         4. Linear Regression in R
         5. Juggling Many Relationships at Once: Multiple Regression
         6. ANOVA: Another Look
         7. Analysis of Covariance: The Final Component of the GLM
      7. Chapter 15: Correlation: The Rise and Fall of Relationships
         1. Scatter plots Again
         2. Understanding Correlation
         3. Correlation and Regression
         4. Testing Hypotheses About Correlation
         5. Correlation in R
         6. Multiple Correlation
         7. Partial Correlation
         8. Partial Correlation in R
         9. Semipartial Correlation
         10. Semipartial Correlation in R
      8. Chapter 16: Curvilinear Regression: When Relationships Get Complicated
         1. What Is a Logarithm?
         2. What Is e?
         3. Power Regression
         4. Exponential Regression
         5. Logarithmic Regression
         6. Polynomial Regression: A Higher Power
         7. Which Model Should You Use?
   6. Part 4: Working with Probability
      1. Chapter 17: Introducing Probability
         1. What Is Probability?
         2. Compound Events
         3. Conditional Probability
         4. Large Sample Spaces
         5. R Functions for Counting Rules
         6. Random Variables: Discrete and Continuous
         7. Probability Distributions and Density Functions
         8. The Binomial Distribution
         9. The Binomial and Negative Binomial in R
         10. Hypothesis Testing with the Binomial Distribution
         11. More on Hypothesis Testing: R versus Tradition
      2. Chapter 18: Introducing Modeling
         1. Modeling a Distribution
         2. A Simulating Discussion
   7. Part 5: The Part of Tens
      1. Chapter 19: Ten Tips for Excel Emigrés
         1. Defining a Vector in R Is Like Naming a Range in Excel
         2. Operating on Vectors Is Like Operating on Named Ranges
         3. Sometimes Statistical Functions Work the Same Way …
         4. … And Sometimes They Don’t
         5. Contrast: Excel and R Work with Different Data Formats
         6. Distribution Functions Are (Somewhat) Similar
         7. A Data Frame Is (Something) Like a Multicolumn Named Range
         8. The sapply() Function Is Like Dragging
         9. Using edit() Is (Almost) Like Editing a Spreadsheet
         10. Use the Clipboard to Import a Table from Excel into R
      2. Chapter 20: Ten Valuable Online R Resources
         1. Websites for R Users
         2. Online Books and Documentation
   8. About the Author
   9. Connect with Dummies
   10. End User License Agreement

Guide

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