Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables – basic probability, and statistical inference – including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area.
In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines.
Cover image Title page Table of Contents Copyright Dedication Biography Preface Acknowledgments Part I: An introduction to statistics and R Chapter 1: What is statistics and why is it important? 1.1. Introduction 1.2. So what is statistics? 1.3. Computation and statistics References Chapter 2: An introduction to R 2.1. Installation 2.2. Classes of data 2.3. Mathematical operations in R 2.4. Variables 2.5. Vectors 2.6. Data frames 2.7. Practice problems 2.8. Conclusion References Part II: Collecting data and loading it into R Chapter 3: Data collection: methods and concerns 3.1. Introduction 3.2. Components of data collection 3.3. Observational studies 3.4. Designed experiments 3.5. Observational studies and experiments: which to use? 3.6. Conclusion References Chapter 4: R tutorial: subsetting data, random numbers, and selecting a random sample 4.1. Introduction 4.2. Subsetting vectors 4.3. Subsetting data frames 4.4. Random numbers in R 4.5. Select a random sample 4.6. Getting help in R 4.7. Practice problems 4.8. Conclusion References Chapter 5: R tutorial: libraries and loading data into R 5.1. Introduction 5.2. Libraries in R 5.3. Loading datasets stored in libraries 5.4. Loading csv files into R 5.5. Practice problems 5.6. Conclusion References Part III: Exploring and describing data Chapter 6: Exploratory data analyses: describing our data 6.1. Introduction 6.2. Parameters and statistics 6.3. Parameters, statistics, and EDA for categorical variables 6.4. Parameters, statistics, and EDA for a single quantitative variable 6.5. Visual summaries for a single quantitative variables 6.6. Identifying outliers 6.7. Exploring relationships between variables 6.8. Exploring association between categorical predictor and quantitative response 6.9. Exploring association between two quantitative variables 6.10. Conclusion References Chapter 7: R tutorial: EDA in R 7.1. Introduction 7.2. Frequency and contingency tables in R 7.3. Numerical exploratory analyses in R 7.4. Missing data 7.5. Practice problems 7.6. Graphical exploratory analyses in R 7.7. Boxplots 7.8. Practice problems 7.9. Conclusion References Part IV: Mechanisms of inference Chapter 8: An incredibly brief introduction to probability 8.1. Introduction 8.2. Random phenomena, probability, and the Law of Large Numbers 8.3. What is the role of probability in inference? 8.4. Calculating probability and the axioms of probability 8.5. Random variables and probability distributions 8.6. The binomial distribution 8.7. The normal distribution 8.8. Practice problems 8.9. Conclusion Chapter 9: Sampling distributions, or why exploratory analyses are not enough 9.1. Introduction 9.2. Sampling distributions 9.3. Properties of sampling distributions and the central limit theorem 9.4. Practice problems 9.5. Conclusion Chapter 10: The idea behind testing hypotheses 10.1. Introduction 10.2. A lady tasting tea 10.3. Hypothesis testing 10.4. Practice problems 10.5. Conclusion References Chapter 11: Making hypothesis testing work with the central limit theorem 11.1. Introduction 11.2. Recap of the normal distribution 11.3. Getting probabilities from the normal distributions 11.4. Connecting data to p-values 11.5. Conclusion Chapter 12: The idea of interval estimates 12.1. Introduction 12.2. Point and interval estimates 12.3. When intervals are “right” 12.4. Confidence intervals 12.5. Creating confidence intervals 12.6. Interpreting confidence intervals 12.7. Practice problems 12.8. Conclusion References Part V: Statistical inference Chapter 13: Hypothesis tests for a single parameter 13.1. Introduction 13.2. One-sample test for proportions 13.3. One-sample t-test for means 13.4. Conclusion References Chapter 14: Confidence intervals for a single parameter 14.1. Introduction 14.2. Confidence interval for p 14.3. Confidence interval for μ 14.4. Other uses of confidence intervals 14.5. Conclusion References Chapter 15: Hypothesis tests for two parameters 15.1. Introduction 15.2. Two-sample test for proportions 15.3. Two-sample t-test for means 15.4. Paired t-test for means 15.5. Conclusion References Chapter 16: Confidence intervals for two parameters 16.1. Introduction 16.2. Confidence interval for p1−p2 16.3. Confidence interval for μ1−μ2 16.4. Confidence intervals for μD 16.5. Confidence intervals for μ1−μ2, μD, and hypothesis testing 16.6. Conclusion References Chapter 17: R tutorial: statistical inference in R 17.1. Introduction 17.2. Choosing the right test 17.3. Inference for proportions 17.4. Inference for means 17.5. Conclusion References Chapter 18: Inference for two quantitative variables 18.1. Introduction 18.2. Test for correlations 18.3. Confidence intervals for correlations 18.4. Test for correlations in R 18.5. Confidence intervals for correlations 18.6. Practice problems 18.7. Conclusion References Chapter 19: Simple linear regression 19.1. Introduction 19.2. Basic of lines 19.3. The simple linear regression model 19.4. Estimating the regression model 19.5. Regression in R 19.6. Practice problems 19.7. Using regression to create predictions 19.8. Practice problems 19.9. The assumptions of regression 19.10. Inference for regression 19.11. How good is our regression? 19.12. Practice problems 19.13. Conclusion References Chapter 20: Statistics: the world beyond this book 20.1. Questions beyond the techniques of this book 20.2. The answers statistics gives 20.3. Where does this leave us? References Appendix A: Solutions to practice problems Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Appendix B: List of R datasets References References Index
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