Statistical Analysis with R For Dummies
Description
Simplify stats and learn how to graph, analyze, and interpret data the easy way
Statistical Analysis with R For Dummies makes stats approachable by combining clear explanations with practical applications. You'll learn how to download and use R and RStudio-two free, open-source tools-to learn statistics concepts, create graphs, test hypotheses, and draw meaningful conclusions. Get started by learning the basics of statistics and R, calculate descriptive statistics, and use inferential statistics to test hypotheses. Then, visualize it all with graphs and charts. This Dummies guide is your well-marked path to sailing through statistics.
- Get clear explanations of the basics of statistics and data analysis
- Learn how to analyze and visualize data with R, step by step
- Create charts, graphs, and summaries to interpret results
- Explore hypothesis testing, and prediction techniques
This is the perfect introduction to R for students, professionals, and the stat-curious.
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Joseph Schmuller is a cognitive scientist and statistical analyst who creates online learning tools and books on data science. He is the author of R All-in One For Dummies, all five editions of Statistical Analysis with Excel For Dummies, Statistical Analysis with R For Dummies, and R Projects For Dummies, among others.
Content
Introduction 1
Part 1: Getting Started with Statistical Analysis with R. 7
Chapter 1: Data, Statistics, and Decisions 9
Chapter 2: R: What It Does and How It Does It 17
Part 2: Describing Data 47
Chapter 3: Getting Graphic 49
Chapter 4: Finding Your Center 79
Chapter 5: Deviating from the Average 91
Chapter 6: Meeting Standards and Standings 101
Chapter 7: Summarizing It All 113
Chapter 8: What's Normal? 133
Part 3: Drawing Conclusions from Data 153
Chapter 9: The Confidence Game: Estimation 155
Chapter 10: One-Sample Hypothesis Testing 171
Chapter 11: Two-Sample Hypothesis Testing 197
Chapter 12: Testing More than Two Samples 223
Chapter 13: More Complicated Testing 249
Chapter 14: Regression: Linear, Multiple, and the General Linear Model 273
Chapter 15: Correlation: The Rise and Fall of Relationships 311
Chapter 16: Curvilinear Regression: When Relationships Get Complicated 333
Part 4: Working with Probability 357
Chapter 17: Introducing Probability 359
Chapter 18: Introducing Modeling 381
Chapter 19: Probability Meets Regression: Logistic Regression 403
Part 5: The Part of Tens 413
Chapter 20: Ten Tips for Excel Émigrés 415
Chapter 21: Ten Valuable Online R Resources 429
Index 433
