Cover Page: i
Introduction Page: xiii
About This Book Page: 1
Similarity with This Other For Dummies Book Page: 2
What You Can Safely Skip Page: 2
Foolish Assumptions Page: 2
How This Book Is Organized Page: 3
Icons Used in This Book Page: 4
Where to Go from Here Page: 5
Part 1: Getting Started with Statistical Analysis with R Page: 7
Chapter 1: Data, Statistics, and Decisions Page: 8
The Statistical (and Related) Notions You Just Have to Know Page: 10
Inferential Statistics: Testing Hypotheses Page: 14
Chapter 2: R: What It Does and How It Does It Page: 16
Downloading R and RStudio Page: 18
A Session with R Page: 21
R Functions Page: 26
User-Defined Functions Page: 28
Comments Page: 29
R Structures Page: 29
Packages Page: 39
More Packages Page: 42
R Formulas Page: 43
Reading and Writing Page: 44
Part 2: Describing Data Page: 49
Chapter 3: Getting Graphic Page: 50
Finding Patterns Page: 51
Base R Graphics Page: 57
Graduating to ggplot2 Page: 71
Wrapping Up Page: 89
Chapter 4: Finding Your Center Page: 89
Means: The Lure of Averages Page: 91
The Average in R: mean() Page: 93
Medians: Caught in the Middle Page: 99
The Median in R: median() Page: 100
Statistics à la Mode Page: 101
The Mode in R Page: 101
Chapter 5: Deviating from the Average Page: 101
Measuring Variation Page: 104
Back to the Roots: Standard Deviation Page: 108
Standard Deviation in R Page: 109
Conditions, Conditions, Conditions … Page: 110
Chapter 6: Meeting Standards and Standings Page: 110
Catching Some Z’s Page: 112
Standard Scores in R Page: 114
Where Do You Stand? Page: 117
Summarizing Page: 121
Chapter 7: Summarizing It All Page: 122
How Many? Page: 123
The High and the Low Page: 125
Living in the Moments Page: 125
Tuning in the Frequency Page: 131
Summarizing a Data Frame Page: 139
Chapter 8: What’s Normal? Page: 142
Hitting the Curve Page: 143
Working with Normal Distributions Page: 147
A Distinguished Member of the Family Page: 158
Part 3: Drawing Conclusions from Data Page: 161
Chapter 9: The Confidence Game: Estimation Page: 162
Understanding Sampling Distributions Page: 164
An EXTREMELY Important Idea: The Central Limit Theorem Page: 165
Confidence: It Has Its Limits! Page: 173
Fit to a t Page: 175
Chapter 10: One-Sample Hypothesis Testing Page: 177
Hypotheses, Tests, and Errors Page: 179
Hypothesis Tests and Sampling Distributions Page: 181
Catching Some Z’s Again Page: 183
Z Testing in R Page: 185
t for One Page: 187
t Testing in R Page: 188
Working with t-Distributions Page: 189
Visualizing t-Distributions Page: 190
Testing a Variance Page: 198
Working with Chi-Square Distributions Page: 201
Visualizing Chi-Square Distributions Page: 201
Chapter 11: Two-Sample Hypothesis Testing Page: 204
Hypotheses Built for Two Page: 205
Sampling Distributions Revisited Page: 206
t for Two Page: 212
Like Peas in a Pod: Equal Variances Page: 212
t-Testing in R Page: 214
A Matched Set: Hypothesis Testing for Paired Samples Page: 220
Paired Sample t-testing in R Page: 222
Testing Two Variances Page: 222
Working with F-Distributions Page: 226
Visualizing F-Distributions Page: 226
Chapter 12: Testing More than Two Samples Page: 230
Testing More Than Two Page: 231
ANOVA in R Page: 237
Another Kind of Hypothesis, Another Kind of Test Page: 244
Getting Trendy Page: 250
Trend Analysis in R Page: 254
Chapter 13: More Complicated Testing Page: 254
Cracking the Combinations Page: 255
Two-Way ANOVA in R Page: 259
Two Kinds of Variables … at Once Page: 263
After the Analysis Page: 269
Multivariate Analysis of Variance Page: 270
Chapter 14: Regression: Linear, Multiple, and the General Linear Model Page: 276
The Plot of Scatter Page: 277
Graphing Lines Page: 279
Regression: What a Line! Page: 281
Linear Regression in R Page: 290
Juggling Many Relationships at Once: Multiple Regression Page: 295
ANOVA: Another Look Page: 301
Analysis of Covariance: The Final Component of the GLM Page: 305
Chapter 15: Correlation: The Rise and Fall of Relationships Page: 312
Scatter plots Again Page: 313
Understanding Correlation Page: 314
Correlation and Regression Page: 316
Testing Hypotheses About Correlation Page: 319
Correlation in R Page: 322
Multiple Correlation Page: 326
Partial Correlation Page: 329
Partial Correlation in R Page: 330
Semipartial Correlation Page: 331
Semipartial Correlation in R Page: 332
Chapter 16: Curvilinear Regression: When Relationships Get Complicated Page: 333
What Is a Logarithm? Page: 336
What Is e? Page: 338
Power Regression Page: 341
Exponential Regression Page: 346
Logarithmic Regression Page: 350
Polynomial Regression: A Higher Power Page: 354
Which Model Should You Use? Page: 358
Part 4: Working with Probability Page: 359
Chapter 17: Introducing Probability Page: 360
What Is Probability? Page: 361
Compound Events Page: 363
Conditional Probability Page: 365
Large Sample Spaces Page: 366
R Functions for Counting Rules Page: 369
Random Variables: Discrete and Continuous Page: 371
Probability Distributions and Density Functions Page: 371
The Binomial Distribution Page: 374
The Binomial and Negative Binomial in R Page: 375
Hypothesis Testing with the Binomial Distribution Page: 378
More on Hypothesis Testing: R versus Tradition Page: 380
Chapter 18: Introducing Modeling Page: 382
Modeling a Distribution Page: 383
A Simulating Discussion Page: 396
Part 5: The Part of Tens Page: 405
Chapter 19: Ten Tips for Excel Emigrés Page: 406
Defining a Vector in R Is Like Naming a Range in Excel Page: 407
Operating on Vectors Is Like Operating on Named Ranges Page: 408
Sometimes Statistical Functions Work the Same Way … Page: 412
… And Sometimes They Don’t Page: 412
Contrast: Excel and R Work with Different Data Formats Page: 413
Distribution Functions Are (Somewhat) Similar Page: 414
A Data Frame Is (Something) Like a Multicolumn Named Range Page: 416
The sapply() Function Is Like Dragging Page: 417
Using edit() Is (Almost) Like Editing a Spreadsheet Page: 418
Use the Clipboard to Import a Table from Excel into R Page: 419
Chapter 20: Ten Valuable Online R Resources Page: 420
Websites for R Users Page: 421
Online Books and Documentation Page: 423
About the Author Page: 439
Connect with Dummies Page: 442
End User License Agreement Page: 442
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.
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