Statistical Analysis with R For Dummies PDF
Preview Statistical Analysis with R For Dummies
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
Description: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!
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