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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.
Résumé
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 addressesas 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.
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 <p>...