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Visual Statistics Use R!

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Visual Statistics Use R!

The ignorance of the statistical methods can lead to mistakes and misinterpretations. Unfortunately, understanding of these methods is far from common. Many college majors require a course in probability theory and mathematical statistics, but all many of us remember from these courses is horror and/or frustration at complex mathematical formulas filled with Greek letters, some of them wearing hats.

Introductory StatisticsStatistics
Author
Alexey Shipunov
Publisher
Minot State University
Source
LibreTexts
License
CC0 / Public domain
Language
EN
Category
Basic Sciences
Read online Download PDF (open licence)
Contents· 75 sections
  1. 01: Data
  2. 1.01: Origin of the data
  3. 1.02: Population and sample
  4. 1.03: How to obtain the data
  5. 1.04: What to find in the data
  6. 1.05: Answers to exercises
  7. 02: How to process the data
  8. 2.01: General purpose software
  9. 2.02: Statistical software
  10. 2.03: The very short history of the S and R
  11. 2.04: Use advantages and disadvantages of the R
  12. 2.05: How to download and install R
  13. 2.06: How to start with R
  14. 2.07: R and Data
  15. 2.08: R graphics
  16. 2.09: Answers to exercises
  17. 03: Types of Data
  18. 3.01: Degrees hours and kilometers- measurement data
  19. 3.02: Grades and t-shirts- ranked data
  20. 3.03: Colors Names and Sexes - Nominal Data
  21. 3.04: Fractions counts and ranks- secondary data
  22. 3.05: Missing data
  23. 3.06: Outliers and how to find them
  24. 3.07: Changing data- basics of transformations
  25. 3.08: Inside R
  26. 3.09: Answers to exercises
  27. 04: One-Dimensional Data
  28. 4.01: How to Estimate General Tendencies
  29. 4.02: 1-Dimensional Plots
  30. 4.03: Confidence intervals
  31. 4.04: Normality
  32. 4.05: How to create your own functions
  33. 4.06: How good is the proportion
  34. 4.07: Answers to exercises
  35. 05: Two-Dimensional Data - Differences
  36. 5.01: What is a statistical test
  37. 5.02: Is there a difference Comparing two samples
  38. 5.03: If there are More than Two Samples - ANOVA
  39. 5.04: Is there an association Analysis of tables
  40. 5.05: Answers to exercises
  41. 06: Two-Dimensional Data - Models
  42. 6.01: Analysis of Correlation
  43. 6.02: Analysis of regression
  44. 6.03: Probability of the success- logistic regression
  45. 6.04: Answers to exercises
  46. 07: Multidimensional Data - Analysis of Structure
  47. 7.01: How to draw the multivariate data
  48. 7.02: Classification without learning
  49. 7.03: Machine learning
  50. 7.04: Semi-supervised learning
  51. 7.05: Deep Learning
  52. 7.06: How to choose the right method
  53. 7.07: Answers to exercises
  54. 08: Appendix A- Example of R session
  55. 8.01: Starting...
  56. 8.02: Describing...
  57. 8.03: Plotting...
  58. 8.04: Testing...
  59. 8.05: Finishing...
  60. 8.06: Answers to exercises
  61. 09: Appendix B- Ten Years Later or use R script
  62. 9.01: How to make your first R script
  63. 9.02: My R script does not work
  64. 9.03: Common pitfalls in R scripting
  65. 9.04: Good Bad and Not-too-bad
  66. 9.05: Answers to exercises
  67. 10: Appendix C- R fragments
  68. 10.01: R and databases
  69. 10.02: R and time
  70. 10.03: R and Bootstrapping
  71. 10.04: R and shape
  72. 10.05: R and Bayes
  73. 10.06: R DNA and evolution
  74. 10.07: R and reporting
  75. 10.08: Answers to exercises