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.
1. Chapter 2 : An introduction to R - p. Page 6 2. Chapter 3 : Data collection: methods and concerns - p. Page 14 3. Chapter 4 : R tutorial: subsetting data, random numbers, and selecting a random sample - p. Page 25 4. Chapter 5 : R tutorial: libraries and loading data into R - p. Page 35 5. Chapter 6 : Exploratory data analyses: describing our data - p. Page 43 6. Chapter 7 : R tutorial: EDA in R - p. Page 72 7. Chapter 8 : An incredibly brief introduction to probability - p. Page 85 8. Chapter 9 : Sampling distributions, or why exploratory analyses are not enough - p. Page 99 9. Chapter 10 : The idea behind testing hypotheses - p. Page 107 10. Chapter 11 : Making hypothesis testing work with the central limit theorem - p. Page 115 11. Chapter 12 : The idea of interval estimates - p. Page 125 12. Chapter 13 : Hypothesis tests for a single parameter - p. Page 133 13. Chapter 14 : Confidence intervals for a single parameter - p. Page 150 14. Chapter 15 : Hypothesis tests for two parameters - p. Page 162 15. Chapter 16 : Confidence intervals for two parameters - p. Page 185 16. Chapter 17 : R tutorial: statistical inference in R - p. Page 201 17. Chapter 18 : Inference for two quantitative variables - p. Page 214 18. Chapter 19 : Simple linear regression - p. Page 225 19. Chapter 20 : Statistics: the world beyond this book - p. Page 247 20. Appendix A : Solutions to practice problems - p. Page 254 21. Appendix B : List of R datasets - p. Page 267 22. References - p. Page 270 23. Index - p. Page 270
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.
1. Chapter 2 : An introduction to R - p. Page 6 2. Chapter 3 : Data collection: methods and concerns - p. Page 14 3. Chapter 4 : R tutorial: subsetting data, random numbers, and selecting a random sample - p. Page 25 4. Chapter 5 : R tutorial: libraries and loading data into R - p. Page 35 5. Chapter 6 : Exploratory data analyses: describing our data - p. Page 43 6. Chapter 7 : R tutorial: EDA in R - p. Page 72 7. Chapter 8 : An incredibly brief introduction to probability - p. Page 85 8. Chapter 9 : Sampling distributions, or why exploratory analyses are not enough - p. Page 99 9. Chapter 10 : The idea behind testing hypotheses - p. Page 107 10. Chapter 11 : Making hypothesis testing work with the central limit theorem - p. Page 115 11. Chapter 12 : The idea of interval estimates - p. Page 125 12. Chapter 13 : Hypothesis tests for a single parameter - p. Page 133 13. Chapter 14 : Confidence intervals for a single parameter - p. Page 150 14. Chapter 15 : Hypothesis tests for two parameters - p. Page 162 15. Chapter 16 : Confidence intervals for two parameters - p. Page 185 16. Chapter 17 : R tutorial: statistical inference in R - p. Page 201 17. Chapter 18 : Inference for two quantitative variables - p. Page 214 18. Chapter 19 : Simple linear regression - p. Page 225 19. Chapter 20 : Statistics: the world beyond this book - p. Page 247 20. Appendix A : Solutions to practice problems - p. Page 254 21. Appendix B : List of R datasets - p. Page 267 22. References - p. Page 270 23. Index - p. Page 270
