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