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Statistical Analysis with R For Dummies

Book Description

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

    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
        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