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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.

Mathematics
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· 91 sections
  1. 1.6.01: Data Visualization
  2. 1.6.1.01: Anatomy of a Plot
  3. 1.6.1.02: Principles of Good Visualization
  4. 1.6.1.03: Accommodating Human Limitations
  5. 1.6.1.04: Correcting for Other Factors
  6. 1.6.1.05: Suggested readings and videos
  7. 1.6.02: Data
  8. 1.6.2.01: Origin of the data
  9. 1.6.2.02: Population and sample
  10. 1.6.2.03: How to obtain the data
  11. 1.6.2.04: What to find in the data
  12. 1.6.2.05: Answers to exercises
  13. 1.6.03: How to process the data
  14. 1.6.3.01: General purpose software
  15. 1.6.3.02: Statistical software
  16. 1.6.3.03: The very short history of the S and R
  17. 1.6.3.04: Use, advantages and disadvantages of the R
  18. 1.6.3.05: How to download and install R
  19. 1.6.3.06: How to start with R
  20. 1.6.3.07: R and Data
  21. 1.6.3.08: R graphics
  22. 1.6.3.09: Answers to exercises
  23. 1.6.04: Types of Data
  24. 1.6.4.01: Degrees, hours and kilometers- measurement data
  25. 1.6.4.02: Grades and t-shirts- ranked data
  26. 1.6.4.03: Colors, Names and Sexes - Nominal Data
  27. 1.6.4.04: Fractions, counts and ranks- secondary data
  28. 1.6.4.05: Missing data
  29. 1.6.4.06: Outliers, and how to find them
  30. 1.6.4.07: Changing data- basics of transformations
  31. 1.6.4.08: Inside R
  32. 1.6.4.09: Answers to exercises
  33. 1.6.05: One-Dimensional Data
  34. 1.6.5.01: How to Estimate General Tendencies
  35. 1.6.5.02: 1-Dimensional Plots
  36. 1.6.5.03: Confidence intervals
  37. 1.6.5.04: Normality
  38. 1.6.5.05: How to create your own functions
  39. 1.6.5.06: How good is the proportion?
  40. 1.6.5.07: Answers to exercises
  41. 1.6.06: Two-Dimensional Data - Differences
  42. 1.6.6.01: What is a statistical test?
  43. 1.6.6.02: Is there a difference? Comparing two samples
  44. 1.6.6.03: If there are More than Two Samples - ANOVA
  45. 1.6.6.04: Is there an association? Analysis of tables
  46. 1.6.6.05: Answers to exercises
  47. 1.6.07: Two-Dimensional Data - Models
  48. 1.6.7.01: Analysis of Correlation
  49. 1.6.7.02: Analysis of regression
  50. 1.6.7.03: Probability of the success- logistic regression
  51. 1.6.7.04: Answers to exercises
  52. 1.6.08: Multidimensional Data - Analysis of Structure
  53. 1.6.8.01: How to draw the multivariate data
  54. 1.6.8.02: Classification without learning
  55. 1.6.8.03: Machine learning
  56. 1.6.8.04: Semi-supervised learning
  57. 1.6.8.05: Deep Learning
  58. 1.6.8.06: How to choose the right method
  59. 1.6.8.07: Answers to exercises
  60. 1.6.09: Appendices
  61. 1.6.9.01: Example of R session
  62. 1.6.9.1.01: Starting...
  63. 1.6.9.1.02: Describing...
  64. 1.6.9.1.03: Plotting...
  65. 1.6.9.1.04: Testing...
  66. 1.6.9.1.05: Finishing...
  67. 1.6.9.1.06: Answers to exercises
  68. 1.6.9.02: Ten Years Later, or use R script
  69. 1.6.9.2.01: How to make your first R script
  70. 1.6.9.2.02: My R script does not work!
  71. 1.6.9.2.03: Common pitfalls in R scripting
  72. 1.6.9.2.04: Good, Bad, and Not-too-bad
  73. 1.6.9.2.05: Answers to exercises
  74. 1.6.9.03: R fragments
  75. 1.6.9.3.01: R and databases
  76. 1.6.9.3.02: R and time
  77. 1.6.9.3.03: R and Bootstrapping
  78. 1.6.9.3.04: R and shape
  79. 1.6.9.3.05: R and Bayes
  80. 1.6.9.3.06: R, DNA and evolution
  81. 1.6.9.3.07: R and reporting
  82. 1.6.9.3.08: Answers to exercises
  83. 1.6.9.04: Most essential R commands
  84. 1.6.9.05: The short R glossary
  85. 1.6.9.06: References and reference cards
  86. 1.6.10: Examining the Evidence using Graphs and Statistics
  87. 1.6.10.0E: 1.6.10.E: Examining the Evidence using Graphs and Statistics (Exercises)
  88. 1.6.11: Graphical Descriptions of Data
  89. 1.6.11.01: Qualitative Data
  90. 1.6.11.02: Quantitative Data
  91. 1.6.11.03: Other Graphical Representations of Data