Statistical Analysis with R For Dummies, 7 by Schmuller, Joseph -- Read -- Imperial Library of Trantor

Index

Cover Title Page Table of Contents 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

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