Statistical Analysis with R For Dummies

 
 
Standards Information Network (Verlag)
  • erschienen am 3. März 2017
  • |
  • 456 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-33709-6 (ISBN)
 
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!
1. Auflage
  • Englisch
  • Somerset
  • |
  • USA
John Wiley & Sons Inc
  • 9,19 MB
978-1-119-33709-6 (9781119337096)
weitere Ausgaben werden ermittelt
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.
1 - Title Page [Seite 3]
2 - Copyright Page [Seite 4]
3 - Table of Contents [Seite 7]
4 - Introduction [Seite 17]
4.1 - About This Book [Seite 17]
4.2 - Similarity with This Other For Dummies Book [Seite 18]
4.3 - What You Can Safely Skip [Seite 18]
4.4 - Foolish Assumptions [Seite 18]
4.5 - How This Book Is Organized [Seite 19]
4.5.1 - Part 1: Getting Started with Statistical Analysis with R [Seite 19]
4.5.2 - Part 2: Describing Data [Seite 19]
4.5.3 - Part 3: Drawing Conclusions from Data [Seite 19]
4.5.4 - Part 4: Working with Probability [Seite 19]
4.5.5 - Part 5: The Part of Tens [Seite 20]
4.5.6 - Online Appendix A: More on Probability [Seite 20]
4.5.7 - Online Appendix B: Non-Parametric Statistics [Seite 20]
4.5.8 - Online Appendix C: Ten Topics That Just Didn't Fit in Any Other Chapter [Seite 20]
4.6 - Icons Used in This Book [Seite 20]
4.7 - Where to Go from Here [Seite 21]
5 - Part 1 Getting Started with Statistical Analysis with R [Seite 23]
5.1 - Chapter 1 Data, Statistics, and Decisions [Seite 25]
5.1.1 - The Statistical (and Related) Notions You Just Have to Know [Seite 26]
5.1.1.1 - Samples and populations [Seite 26]
5.1.1.2 - Variables: Dependent and independent [Seite 27]
5.1.1.3 - Types of data [Seite 28]
5.1.1.4 - A little probability [Seite 29]
5.1.2 - Inferential Statistics: Testing Hypotheses [Seite 30]
5.1.2.1 - Null and alternative hypotheses [Seite 30]
5.1.2.2 - Two types of error [Seite 31]
5.2 - Chapter 2 R: What It Does and How It Does It [Seite 33]
5.2.1 - Downloading R and RStudio [Seite 34]
5.2.2 - A Session with R [Seite 37]
5.2.2.1 - The working directory [Seite 37]
5.2.2.2 - So let's get started, already [Seite 38]
5.2.2.3 - Missing data [Seite 42]
5.2.3 - R Functions [Seite 42]
5.2.4 - User-Defined Functions [Seite 44]
5.2.5 - Comments [Seite 45]
5.2.6 - R Structures [Seite 45]
5.2.6.1 - Vectors [Seite 46]
5.2.6.2 - Numerical vectors [Seite 46]
5.2.6.3 - Matrices [Seite 47]
5.2.6.4 - Factors [Seite 49]
5.2.6.5 - Lists [Seite 50]
5.2.6.6 - Lists and statistics [Seite 51]
5.2.6.7 - Data frames [Seite 52]
5.2.7 - Packages [Seite 55]
5.2.8 - More Packages [Seite 58]
5.2.9 - R Formulas [Seite 59]
5.2.10 - Reading and Writing [Seite 60]
5.2.10.1 - Spreadsheets [Seite 60]
5.2.10.2 - CSV files [Seite 62]
5.2.10.3 - Text files [Seite 63]
6 - Part 2 Describing Data [Seite 65]
6.1 - Chapter 3 Getting Graphic [Seite 67]
6.1.1 - Finding Patterns [Seite 67]
6.1.1.1 - Graphing a distribution [Seite 68]
6.1.1.2 - Bar-hopping [Seite 69]
6.1.1.3 - Slicing the pie [Seite 70]
6.1.1.4 - The plot of scatter [Seite 71]
6.1.1.5 - Of boxes and whiskers [Seite 72]
6.1.2 - Base R Graphics [Seite 73]
6.1.2.1 - Histograms [Seite 73]
6.1.2.2 - Adding graph features [Seite 75]
6.1.2.3 - Bar plots [Seite 76]
6.1.2.4 - Pie graphs [Seite 78]
6.1.2.5 - Dot charts [Seite 78]
6.1.2.6 - Bar plots revisited [Seite 80]
6.1.2.7 - Scatter plots [Seite 83]
6.1.2.8 - Box plots [Seite 87]
6.1.3 - Graduating to ggplot2 [Seite 87]
6.1.3.1 - Histograms [Seite 88]
6.1.3.2 - Bar plots [Seite 90]
6.1.3.3 - Dot charts [Seite 91]
6.1.3.4 - Bar plots re-revisited [Seite 94]
6.1.3.5 - Scatter plots [Seite 98]
6.1.3.6 - Box plots [Seite 102]
6.1.4 - Wrapping Up [Seite 105]
6.2 - Chapter 4 Finding Your Center [Seite 107]
6.2.1 - Means: The Lure of Averages [Seite 107]
6.2.2 - The Average in R: mean() [Seite 109]
6.2.2.1 - What's your condition? [Seite 109]
6.2.2.2 - Eliminate $-signs forth with() [Seite 110]
6.2.2.3 - Exploring the data [Seite 111]
6.2.2.4 - Outliers: The flaw of averages [Seite 112]
6.2.2.5 - Other means to an end [Seite 113]
6.2.3 - Medians: Caught in the Middle [Seite 115]
6.2.4 - The Median in R: median() [Seite 116]
6.2.5 - Statistics à la Mode [Seite 117]
6.2.6 - The Mode in R [Seite 117]
6.3 - Chapter 5 Deviating from the Average [Seite 119]
6.3.1 - Measuring Variation [Seite 120]
6.3.1.1 - Averaging squared deviations: Variance and how to calculate it [Seite 120]
6.3.1.2 - Sample variance [Seite 123]
6.3.1.3 - Variance in R [Seite 123]
6.3.2 - Back to the Roots: Standard Deviation [Seite 124]
6.3.2.1 - Population standard deviation [Seite 124]
6.3.2.2 - Sample standard deviation [Seite 125]
6.3.3 - Standard Deviation in R [Seite 125]
6.3.4 - Conditions, Conditions, Conditions . . . [Seite 126]
6.4 - Chapter 6 Meeting Standards and Standings [Seite 127]
6.4.1 - Catching Some Z's [Seite 128]
6.4.1.1 - Characteristics of z-scores [Seite 128]
6.4.1.2 - Bonds versus the Bambino [Seite 129]
6.4.1.3 - Exam scores [Seite 130]
6.4.2 - Standard Scores in R [Seite 130]
6.4.3 - Where Do You Stand? [Seite 133]
6.4.3.1 - Ranking in R [Seite 133]
6.4.3.2 - Tied scores [Seite 133]
6.4.3.3 - Nth smallest, Nth largest [Seite 134]
6.4.3.4 - Percentiles [Seite 134]
6.4.3.5 - Percent ranks [Seite 136]
6.4.4 - Summarizing [Seite 137]
6.5 - Chapter 7 Summarizing It All [Seite 139]
6.5.1 - How Many? [Seite 139]
6.5.2 - The High and the Low [Seite 141]
6.5.3 - Living in the Moments [Seite 141]
6.5.3.1 - A teachable moment [Seite 142]
6.5.3.2 - Back to descriptives [Seite 142]
6.5.3.3 - Skewness [Seite 143]
6.5.3.4 - Kurtosis [Seite 146]
6.5.4 - Tuning in the Frequency [Seite 147]
6.5.4.1 - Nominal variables: table() et al [Seite 147]
6.5.4.2 - Numerical variables: hist() [Seite 148]
6.5.4.3 - Numerical variables: stem() [Seite 154]
6.5.5 - Summarizing a Data Frame [Seite 155]
6.6 - Chapter 8 What's Normal? [Seite 159]
6.6.1 - Hitting the Curve [Seite 159]
6.6.1.1 - Digging deeper [Seite 160]
6.6.1.2 - Parameters of a normal distribution [Seite 161]
6.6.2 - Working with Normal Distributions [Seite 163]
6.6.2.1 - Distributions in R [Seite 163]
6.6.2.2 - Normal density function [Seite 163]
6.6.2.3 - Cumulative density function [Seite 168]
6.6.2.4 - Quantiles of normal distributions [Seite 171]
6.6.2.5 - Random sampling [Seite 172]
6.6.3 - A Distinguished Member of the Family [Seite 174]
7 - Part 3 Drawing Conclusions from Data [Seite 177]
7.1 - Chapter 9 The Confidence Game: Estimation [Seite 179]
7.1.1 - Understanding Sampling Distributions [Seite 180]
7.1.2 - An EXTREMELY Important Idea: The Central Limit Theorem [Seite 181]
7.1.2.1 - (Approximately) Simulating the central limit theorem [Seite 183]
7.1.2.2 - Predictions of the central limit theorem [Seite 187]
7.1.3 - Confidence: It Has Its Limits! [Seite 189]
7.1.3.1 - Finding confidence limits for a mean [Seite 189]
7.1.4 - Fit to a t [Seite 191]
7.2 - Chapter 10 One-Sample Hypothesis Testing [Seite 195]
7.2.1 - Hypotheses, Tests, and Errors [Seite 195]
7.2.2 - Hypothesis Tests and Sampling Distributions [Seite 197]
7.2.3 - Catching Some Z's Again [Seite 199]
7.2.4 - Z Testing in R [Seite 201]
7.2.5 - t for One [Seite 203]
7.2.6 - t Testing in R [Seite 204]
7.2.7 - Working with t-Distributions [Seite 205]
7.2.8 - Visualizing t-Distributions [Seite 206]
7.2.8.1 - Plotting t in base R graphics [Seite 207]
7.2.8.2 - Plotting t in ggplot2 [Seite 208]
7.2.8.3 - One more thing about ggplot2 [Seite 213]
7.2.9 - Testing a Variance [Seite 214]
7.2.9.1 - Testing in R [Seite 215]
7.2.10 - Working with Chi-Square Distributions [Seite 217]
7.2.11 - Visualizing Chi-Square Distributions [Seite 217]
7.2.11.1 - Plotting chi-square in base R graphics [Seite 218]
7.2.11.2 - Plotting chi-square in ggplot2 [Seite 219]
7.3 - Chapter 11 Two-Sample Hypothesis Testing [Seite 221]
7.3.1 - Hypotheses Built for Two [Seite 221]
7.3.2 - Sampling Distributions Revisited [Seite 222]
7.3.2.1 - Applying the central limit theorem [Seite 223]
7.3.2.2 - Z's once more [Seite 224]
7.3.2.3 - Z-testing for two samples in R [Seite 226]
7.3.3 - t for Two [Seite 228]
7.3.4 - Like Peas in a Pod: Equal Variances [Seite 228]
7.3.5 - t-Testing in R [Seite 230]
7.3.5.1 - Working with two vectors [Seite 230]
7.3.5.2 - Working with a data frame and a formula [Seite 231]
7.3.5.3 - Visualizing the results [Seite 232]
7.3.5.4 - Like p's and q's: Unequal variances [Seite 235]
7.3.6 - A Matched Set: Hypothesis Testing for Paired Samples [Seite 236]
7.3.7 - Paired Sample t-testing in R [Seite 238]
7.3.8 - Testing Two Variances [Seite 238]
7.3.8.1 - F-testing in R [Seite 240]
7.3.8.2 - F in conjunction with t [Seite 241]
7.3.9 - Working with F-Distributions [Seite 242]
7.3.10 - Visualizing F-Distributions [Seite 242]
7.4 - Chapter 12 Testing More than Two Samples [Seite 247]
7.4.1 - Testing More Than Two [Seite 247]
7.4.1.1 - A thorny problem [Seite 248]
7.4.1.2 - A solution [Seite 249]
7.4.1.3 - Meaningful relationships [Seite 253]
7.4.2 - ANOVA in R [Seite 253]
7.4.2.1 - Visualizing the results [Seite 255]
7.4.2.2 - After the ANOVA [Seite 255]
7.4.2.3 - Contrasts in R [Seite 258]
7.4.2.4 - Unplanned comparisons [Seite 259]
7.4.3 - Another Kind of Hypothesis, Another Kind of Test [Seite 260]
7.4.3.1 - Working with repeated measures ANOVA [Seite 261]
7.4.3.2 - Repeated measures ANOVA in R [Seite 263]
7.4.3.3 - Visualizing the results [Seite 265]
7.4.4 - Getting Trendy [Seite 266]
7.4.5 - Trend Analysis in R [Seite 270]
7.5 - Chapter 13 More Complicated Testing [Seite 271]
7.5.1 - Cracking the Combinations [Seite 271]
7.5.1.1 - Interactions [Seite 273]
7.5.1.2 - The analysis [Seite 273]
7.5.2 - Two-Way ANOVA in R [Seite 275]
7.5.2.1 - Visualizing the two-way results [Seite 277]
7.5.3 - Two Kinds of Variables . . . at Once [Seite 279]
7.5.3.1 - Mixed ANOVA in R [Seite 282]
7.5.3.2 - Visualizing the Mixed ANOVA results [Seite 284]
7.5.4 - After the Analysis [Seite 285]
7.5.5 - Multivariate Analysis of Variance [Seite 286]
7.5.5.1 - MANOVA in R [Seite 287]
7.5.5.2 - Visualizing the MANOVA results [Seite 289]
7.5.5.3 - After the analysis [Seite 291]
7.6 - Chapter 14 Regression: Linear, Multiple, and the General Linear Model [Seite 293]
7.6.1 - The Plot of Scatter [Seite 293]
7.6.2 - Graphing Lines [Seite 295]
7.6.3 - Regression: What a Line! [Seite 297]
7.6.3.1 - Using regression for forecasting [Seite 299]
7.6.3.2 - Variation around the regression line [Seite 299]
7.6.3.3 - Testing hypotheses about regression [Seite 301]
7.6.4 - Linear Regression in R [Seite 306]
7.6.4.1 - Features of the linear model [Seite 308]
7.6.4.2 - Making predictions [Seite 308]
7.6.4.3 - Visualizing the scatter plot and regression line [Seite 309]
7.6.4.4 - Plotting the residuals [Seite 310]
7.6.5 - Juggling Many Relationships at Once: Multiple Regression [Seite 311]
7.6.5.1 - Multiple regression in R [Seite 313]
7.6.5.2 - Making predictions [Seite 314]
7.6.5.3 - Visualizing the 3D scatter plot and regression plane [Seite 314]
7.6.6 - ANOVA: Another Look [Seite 317]
7.6.7 - Analysis of Covariance: The Final Component of the GLM [Seite 321]
7.6.7.1 - But wait - there's more [Seite 327]
7.7 - Chapter 15 Correlation: The Rise and Fall of Relationships [Seite 329]
7.7.1 - Scatter plots Again [Seite 329]
7.7.2 - Understanding Correlation [Seite 330]
7.7.3 - Correlation and Regression [Seite 332]
7.7.4 - Testing Hypotheses About Correlation [Seite 335]
7.7.4.1 - Is a correlation coefficient greater than zero? [Seite 335]
7.7.4.2 - Do two correlation coefficients differ? [Seite 336]
7.7.5 - Correlation in R [Seite 338]
7.7.5.1 - Calculating a correlation coefficient [Seite 338]
7.7.5.2 - Testing a correlation coefficient [Seite 338]
7.7.5.3 - Testing the difference between two correlation coefficients [Seite 339]
7.7.5.4 - Calculating a correlation matrix [Seite 340]
7.7.5.5 - Visualizing correlation matrices [Seite 340]
7.7.6 - Multiple Correlation [Seite 342]
7.7.6.1 - Multiple correlation in R [Seite 343]
7.7.6.2 - Adjusting R-squared [Seite 344]
7.7.7 - Partial Correlation [Seite 345]
7.7.8 - Partial Correlation in R [Seite 346]
7.7.9 - Semipartial Correlation [Seite 347]
7.7.10 - Semipartial Correlation in R [Seite 348]
7.8 - Chapter 16 Curvilinear Regression: When Relationships Get Complicated [Seite 351]
7.8.1 - What Is a Logarithm? [Seite 352]
7.8.2 - What Is e? [Seite 354]
7.8.3 - Power Regression [Seite 357]
7.8.4 - Exponential Regression [Seite 362]
7.8.5 - Logarithmic Regression [Seite 366]
7.8.6 - Polynomial Regression: A Higher Power [Seite 370]
7.8.7 - Which Model Should You Use? [Seite 374]
8 - Part 4 Working with Probability [Seite 375]
8.1 - Chapter 17 Introducing Probability [Seite 377]
8.1.1 - What Is Probability? [Seite 377]
8.1.1.1 - Experiments, trials, events, and sample spaces [Seite 378]
8.1.1.2 - Sample spaces and probability [Seite 378]
8.1.2 - Compound Events [Seite 379]
8.1.2.1 - Union and intersection [Seite 379]
8.1.2.2 - Intersection again [Seite 380]
8.1.3 - Conditional Probability [Seite 381]
8.1.3.1 - Working with the probabilities [Seite 382]
8.1.3.2 - The foundation of hypothesis testing [Seite 382]
8.1.4 - Large Sample Spaces [Seite 382]
8.1.4.1 - Permutations [Seite 383]
8.1.4.2 - Combinations [Seite 384]
8.1.5 - R Functions for Counting Rules [Seite 385]
8.1.6 - Random Variables: Discrete and Continuous [Seite 387]
8.1.7 - Probability Distributions and Density Functions [Seite 387]
8.1.8 - The Binomial Distribution [Seite 390]
8.1.9 - The Binomial and Negative Binomial in R [Seite 391]
8.1.9.1 - Binomial distribution [Seite 391]
8.1.9.2 - Negative binomial distribution [Seite 393]
8.1.10 - Hypothesis Testing with the Binomial Distribution [Seite 394]
8.1.11 - More on Hypothesis Testing: R versus Tradition [Seite 396]
8.2 - Chapter 18 Introducing Modeling [Seite 399]
8.2.1 - Modeling a Distribution [Seite 399]
8.2.1.1 - Plunging into the Poisson distribution [Seite 400]
8.2.1.2 - Modeling with the Poisson distribution [Seite 401]
8.2.1.3 - Testing the model's fit [Seite 404]
8.2.1.4 - A word about chisq.test() [Seite 407]
8.2.1.5 - Playing ball with a model [Seite 408]
8.2.2 - A Simulating Discussion [Seite 412]
8.2.2.1 - Taking a chance: The Monte Carlo method [Seite 412]
8.2.2.2 - Loading the dice [Seite 412]
8.2.2.3 - Simulating the central limit theorem [Seite 417]
9 - Part 5 The Part of Tens [Seite 421]
9.1 - Chapter 19 Ten Tips for Excel Emigrés [Seite 423]
9.1.1 - Defining a Vector in R Is Like Naming a Range in Excel [Seite 423]
9.1.2 - Operating on Vectors Is Like Operating on Named Ranges [Seite 424]
9.1.3 - Sometimes Statistical Functions Work the Same Way . . . [Seite 428]
9.1.4 - . . . And Sometimes They Don't [Seite 428]
9.1.5 - Contrast: Excel and R Work with Different Data Formats [Seite 429]
9.1.6 - Distribution Functions Are (Somewhat) Similar [Seite 430]
9.1.7 - A Data Frame Is (Something) Like a Multicolumn Named Range [Seite 432]
9.1.8 - The sapply() Function Is Like Dragging [Seite 433]
9.1.9 - Using edit() Is (Almost) Like Editing a Spreadsheet [Seite 434]
9.1.10 - Use the Clipboard to Import a Table from Excel into R [Seite 435]
9.2 - Chapter 20 Ten Valuable Online R Resources [Seite 437]
9.2.1 - Websites for R Users [Seite 437]
9.2.1.1 - R-bloggers [Seite 437]
9.2.1.2 - Microsoft R Application Network [Seite 438]
9.2.1.3 - Quick-R [Seite 438]
9.2.1.4 - RStudio Online Learning [Seite 438]
9.2.1.5 - Stack Overflow [Seite 438]
9.2.2 - Online Books and Documentation [Seite 439]
9.2.2.1 - R manuals [Seite 439]
9.2.2.2 - R documentation [Seite 439]
9.2.2.3 - RDocumentation [Seite 439]
9.2.2.4 - YOU CANanalytics [Seite 439]
9.2.2.5 - The R Journal [Seite 440]
10 - Index [Seite 441]
11 - EULA [Seite 459]

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