Visual Statistics Use R!

by Alexey Shipunov

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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.

Description from LibreTexts.

Publisher
Minot State University
Subjects
Mathematics
Licence
Public domain, as LibreTexts states it
Who can read
Teens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. 00: Front Matter
  2. Table of Contents
  3. 1.6.1: Data Visualization
  4. 1.6.1.1: Anatomy of a Plot
  5. 1.6.1.2: Principles of Good Visualization
  6. 1.6.1.3: Accommodating Human Limitations
  7. 1.6.1.4: Correcting for Other Factors
  8. 1.6.1.5: Suggested readings and videos
  9. 1.6.2: Data
  10. 1.6.2.1: Origin of the data
  11. 1.6.2.2: Population and sample
  12. 1.6.2.3: How to obtain the data
  13. 1.6.2.4: What to find in the data
  14. 1.6.2.5: Answers to exercises
  15. 1.6.3: How to process the data
  16. 1.6.3.1: General purpose software
  17. 1.6.3.2: Statistical software
  18. 1.6.3.3: The very short history of the S and R
  19. 1.6.3.4: Use, advantages and disadvantages of the R
  20. 1.6.3.5: How to download and install R
  21. 1.6.3.6: How to start with R
  22. 1.6.3.7: R and Data
  23. 1.6.3.8: R graphics
  24. 1.6.3.9: Answers to exercises
  25. 1.6.4: Types of Data
  26. 1.6.4.1: Degrees, hours and kilometers- measurement data
  27. 1.6.4.2: Grades and t-shirts- ranked data
  28. 1.6.4.3: Colors, Names and Sexes - Nominal Data
  29. 1.6.4.4: Fractions, counts and ranks- secondary data
  30. 1.6.4.5: Missing data
  31. 1.6.4.6: Outliers, and how to find them
  32. 1.6.4.7: Changing data- basics of transformations
  33. 1.6.4.8: Inside R
  34. 1.6.4.9: Answers to exercises
  35. 1.6.5: One-Dimensional Data
  36. 1.6.5.1: How to Estimate General Tendencies
  37. 1.6.5.2: 1-Dimensional Plots
  38. 1.6.5.3: Confidence intervals
  39. 1.6.5.4: Normality
  40. 1.6.5.5: How to create your own functions
  41. 1.6.5.6: How good is the proportion?
  42. 1.6.5.7: Answers to exercises
  43. 1.6.6: Two-Dimensional Data - Differences
  44. 1.6.6.1: What is a statistical test?
  45. 1.6.6.2: Is there a difference? Comparing two samples
  46. 1.6.6.3: If there are More than Two Samples - ANOVA
  47. 1.6.6.4: Is there an association? Analysis of tables
  48. 1.6.6.5: Answers to exercises
  49. 1.6.7: Two-Dimensional Data - Models
  50. 1.6.7.1: Analysis of Correlation
  51. 1.6.7.2: Analysis of regression
  52. 1.6.7.3: Probability of the success- logistic regression
  53. 1.6.7.4: Answers to exercises
  54. 1.6.8: Multidimensional Data - Analysis of Structure
  55. 1.6.8.1: How to draw the multivariate data
  56. 1.6.8.2: Classification without learning
  57. 1.6.8.3: Machine learning
  58. 1.6.8.4: Semi-supervised learning
  59. 1.6.8.5: Deep Learning
  60. 1.6.8.6: How to choose the right method
  61. 1.6.8.7: Answers to exercises
  62. 1.6.9: Appendices
  63. 1.6.9.1: Example of R session
  64. 1.6.9.1.1: Starting...
  65. 1.6.9.1.2: Describing...
  66. 1.6.9.1.3: Plotting...
  67. 1.6.9.1.4: Testing...
  68. 1.6.9.1.5: Finishing...
  69. 1.6.9.1.6: Answers to exercises
  70. 1.6.9.2: Ten Years Later, or use R script
  71. 1.6.9.2.1: How to make your first R script
  72. 1.6.9.2.2: My R script does not work!
  73. 1.6.9.2.3: Common pitfalls in R scripting
  74. 1.6.9.2.4: Good, Bad, and Not-too-bad
  75. 1.6.9.2.5: Answers to exercises
  76. 1.6.9.3: R fragments
  77. 1.6.9.3.1: R and databases
  78. 1.6.9.3.2: R and time
  79. 1.6.9.3.3: R and Bootstrapping
  80. 1.6.9.3.4: R and shape
  81. 1.6.9.3.5: R and Bayes
  82. 1.6.9.3.6: R, DNA and evolution
  83. 1.6.9.3.7: R and reporting
  84. 1.6.9.3.8: Answers to exercises
  85. 1.6.9.4: Most essential R commands
  86. 1.6.9.5: The short R glossary
  87. 1.6.9.6: References and reference cards
  88. 1.6.10: Examining the Evidence using Graphs and Statistics
  89. 1.6.10.E: Examining the Evidence using Graphs and Statistics (Exercises)
  90. 1.6.11: Graphical Descriptions of Data
  91. 1.6.11.1: Qualitative Data
  92. 1.6.11.2: Quantitative Data
  93. 1.6.11.3: Other Graphical Representations of Data