Statistical Analysis with R For Dummies

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!

Auteur

Joseph Schmuller, PhD, has taught undergraduate and graduate statistics, and has 25 years of IT experience. The author of four editions of Statistical Analysis with Excel For Dummies and three editions of Teach Yourself UML in 24 Hours (SAMS), he has created online coursework for Lynda.com and is a former Editor in Chief of PC AI magazine. He is a Research Scholar at the University of North Florida.

Texte du rabat

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!

Contenu

Introduction 1

About This Book 1

Similarity with This Other For Dummies Book 2

What You Can Safely Skip 2

Foolish Assumptions 2

How This Book Is Organized 3

Part 1: Getting Started with Statistical Analysis with R 3

Part 2: Describing Data 3

Part 3: Drawing Conclusions from Data 3

Part 4: Working with Probability 3

Part 5: The Part of Tens 4

Online Appendix A: More on Probability 4

Online Appendix B: Non-Parametric Statistics 4

Online Appendix C: Ten Topics That Just Didn't Fit in Any Other Chapter 4

Icons Used in This Book 4

Where to Go from Here 5

Part 1: Getting Started with Statistical Analysis with R 7

Chapter 1: Data, Statistics, and Decisions 9

The Statistical (and Related) Notions You Just Have to Know 10

Samples and populations 10

Variables: Dependent and independent 11

Types of data 12

A little probability 13

Inferential Statistics: Testing Hypotheses 14

Null and alternative hypotheses 14

Two types of error 15

Chapter 2: R: What It Does and How It Does It 17

Downloading R and RStudio 18

A Session with R 21

The working directory 21

So let's get started, already 22

Missing data 26

R Functions 26

User-Defined Functions 28

Comments 29

R Structures 29

Vectors 30

Numerical vectors 30

Matrices 31

Factors 33

Lists 34

Lists and statistics 35

Data frames 36

Packages 39

More Packages 42

R Formulas 43

Reading and Writing 44

Spreadsheets 44

CSV files 46

Text files 47

Part 2: Describing Data 49

Chapter 3: Getting Graphic 51

Finding Patterns 51

Graphing a distribution 52

Bar-hopping 53

Slicing the pie 54

The plot of scatter 55

Of boxes and whiskers 56

Base R Graphics 57

Histograms 57

Adding graph features 59

Bar plots 60

Pie graphs 62

Dot charts 62

Bar plots revisited 64

Scatter plots 67

Box plots 71

Graduating to ggplot2 71

Histograms 72

Bar plots 74

Dot charts 75

Bar plots re-revisited 78

Scatter plots 82

Box plots 86

Wrapping Up 89

Chapter 4: Finding Your Center 91

Means: The Lure of Averages 91

The Average in R: mean() 93

What's your condition? 93

Eliminate $-signs forth with() 94

Exploring the data 95

Outliers: The flaw of averages 96

Other means to an end 97

Medians: Caught in the Middle 99

The Median in R: median() 100

Statistics à la Mode 101

The Mode in R 101

Chapter 5: Deviating from the Average 103

Measuring Variation 104

Averaging squared deviations: Variance and how to calculate it 104

Sample variance 107

Variance in R 107

Back to the Roots: Standard Deviation 108

Population standard deviation 108

Sample standard deviation 109

Standard Deviation in R 109

Conditions, Conditions, Conditions 110

Chapter 6: Meeting Standards and Standings 111

Catching Some Z's 112

Characteristics of z-scores 112

Bonds versus the Bambino 113

Exam scores 114

Standard Scores in R 114

Where Do You Stand? 117

Ranking in R 117

Tied scores 117

Nth smallest, Nth largest 118

Percentiles 118

Percent ranks 120

Summarizing 121

Chapter 7: Summarizing It All 123

How Many? 123

The High and the Low 125

Living in the Moments 125

A teachable moment 126

Back to descriptives 126

Skewness 127

Kurtosis 130

Tuning in the Frequency 131

Nominal variables: table() et al 131

Numerical variables: hist() 132

Numerical variables: stem() 138

Summarizing a Data Frame 139

Chapter 8: What's Normal? 143

Hitting the Curve 143

Digging deeper 144

Parameters of a normal distribution 145

Working with Normal Distributions 147

Distributions in R 147

Normal density function 147

Cumulative density function 152

Quantiles of normal distributions 155

Random sampling 156

A Distinguished Member of the Family 158

Part 3: Drawing Conclusions From Data 161

Chapter 9: The Confidence Game: Estimation 163

Understanding Sampling Distributions 164

An EXTREMELY Important Idea: The Central Limit Theorem 165

(Approximately) Simulating the central limit theorem 167

Predictions of the central limit theorem 171

Confidence: It Has Its Limits! 173

Finding confidence limits for a mean 173

Fit to a t 175

Chapter 10: One-Sample Hypothesis Testing 179

Hypotheses, Tests, and Errors 179

Hypothesis Tests and Sampling Distributions 181

Catching Some Z's Again 183

Z Testing in R 185

t for One 187

t Testing in R 188 …