Linear Models and Rurità Kràlovstvì

by Ole Forsberg

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Publisher
Knox College
Subjects
Statistics
Licence
CC BY-NC-SA 4.0, as LibreTexts states it. Non-commercial use only.
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

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  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. Preface
  7. 1: An Introduction to Ruruitania
  8. 1.1: The Background of Ruritania
  9. 1.2: The Geography
  10. 1.3: The Government
  11. 1.4: The Economy
  12. 1.5: US-Ruritanian Relations
  13. 1.6: Illustrations of Analyses
  14. 1.6.1: Illustration- The Missing Kraj
  15. 1.6.2: Illustration- Cows in Děčín
  16. 1.6.3: Illustration- Ruritanian Crops
  17. 1.6.4: Illustration- Wealth in Ruritania
  18. 1.6.5: Illustration- Elections in Ruritania
  19. 1.6.6: Illustration- Insurance in Ruritania
  20. 1.6.7: Illustration- Warmth for the King
  21. 1.7: Conclusion
  22. 2: Part One- Least Squares
  23. 3: Intro to Linear Regression
  24. 3.1: Scalar Representation
  25. 3.2: First Results
  26. 3.3: Our First Assumptions
  27. 3.4: PRE Measures
  28. 3.5: Conclusion
  29. 3.6: End-of-Chapter Materials
  30. 4: Matrices and Linear Regression
  31. 4.1: Matrix Representation
  32. 4.2: Predictions and the Hat Matrix
  33. 4.3: The Geometry of OLS
  34. 4.4: Multicollinearity and Categorical Independent Variables
  35. 4.5: Conclusion
  36. 4.6: End-of-Chapter Materials
  37. 5: Improved! Now with Probabilities
  38. 5.1: Probability Distributions
  39. 5.2: Test Statistics and Hypothesis Testing
  40. 5.3: Confidence Intervals
  41. 5.4: The Working-Hotelling Bands
  42. 5.5: Conclusion
  43. 5.6: End-of-Chapter Materials
  44. 6: Dood! Check the Requirements
  45. 6.1: Normality
  46. 6.2: Functional Fit
  47. 6.3: Homoskedasticity
  48. 6.4: Multicollinearity
  49. 6.5: Conclusion
  50. 6.6: End-of-Chapter Materials
  51. 7: Time for Some Examples
  52. 7.1: Violent Crime
  53. 7.2: Violent Crime, Wealth, Region
  54. 7.3: Cows in Děčín
  55. 7.4: Conclusion
  56. 7.5: End-of-Chapter Materials
  57. 8: Fixing the Violations
  58. 8.1: The Issue of Boundedness
  59. 8.2: The South Sudanese Referendum
  60. 8.3: Adjustments for Heteroskedasticity
  61. 8.4: Conclusion
  62. 8.5: End-of-Chapter Materials
  63. 9: Beyond the Ordinary
  64. 10: Other Least Squares
  65. 10.1: Ordinary Least Squares
  66. 10.2: Weighted Least Squares
  67. 10.3: Generalized Least Squares
  68. 10.4: May the (Strong) Force be with You
  69. 10.5: Elections in Ruritania
  70. 10.6: Conclusion
  71. 10.7: End-of-Chapter Materials
  72. 11: Quantile Regression
  73. 11.1: Parameter Estimation
  74. 11.2: Quantile Regression
  75. 11.3: Conclusion
  76. 11.4: End-of-Chapter Materials
  77. 12: Maximizing the Likelihood
  78. 12.1: The Likelihood
  79. 12.2: The MLE and the CLM
  80. 12.3: Conclusion
  81. 12.4: End-of-Chapter Materials
  82. 13: Beyond the Classical Model
  83. 14: Generalized Linear Models
  84. 14.1: The CLM and the GLM
  85. 14.2: The Requirements for GLMs
  86. 14.3: Assumptions of GLMs
  87. 14.4: The Gaussian Distribution
  88. 14.5: Generalized Linear Models in R
  89. 14.6: Conclusion
  90. 14.7: End-of-Chapter Materials
  91. 15: Binary Dependent Variables
  92. 15.1: Binary Dependent Variables
  93. 15.2: Latent Variable Modeling
  94. 15.3: The Mathematics
  95. 15.4: Modeling with the Logit
  96. 15.5: Measuring Accuracy
  97. 15.6: Modeling with Other Links
  98. 15.7: Model Selection
  99. 15.8: Conclusion
  100. 15.9: End-of-Chapter Material
  101. 16: Binomial Dependent Variables
  102. 16.1: Binomial Distribution
  103. 16.2: The Mathematics
  104. 16.3: O Canada!
  105. 16.4: Sri Lanka in 2010
  106. 16.5: Beta-Binomial Regression
  107. 16.6: Conclusion
  108. 16.7: End-of-Chapter Materials
  109. 17: Count Dependent Variables
  110. 17.1: Linear or Poisson Regression?
  111. 17.2: The Mathematics
  112. 17.3: Overdispersion
  113. 17.4: Body Counts
  114. 17.5: The Bias-Variance Tradeoff
  115. 17.6: Conclusion
  116. 17.7: End-of-Chapter Materials
  117. 18: Nominal and Ordinal Dependent Variables
  118. 18.1: Nominal Dependent Variables
  119. 18.2: Ordinal Dependent Variables
  120. 18.3: Cattle Feed
  121. 18.4: The State University of Ruritania
  122. 18.5: Conclusion
  123. 18.6: End-of-Chapter Materials
  124. 19: Appendices
  125. 20: The Appendix of Matrices
  126. 20.1: Matrix Basics
  127. 20.2: Addition
  128. 20.3: Multiplication
  129. 20.4: Other Terms and Operations
  130. 20.5: Consequences
  131. 20.6: Statistics in Matrices
  132. 20.7: End-of-Appendix Materials
  133. 21: The Appendix of R
  134. 21.1: Installing R
  135. 21.2: R Packages
  136. 21.3: R Functions
  137. 21.4: Programming Practice
  138. 21.4.1: Plotting a Distribution
  139. 21.4.2: Functions of RVs
  140. 21.4.3: Effects of Rounding
  141. 21.4.4: Algorithmic Thinking
  142. 21.4.5: Distribution of Extreme Values
  143. 21.4.6: Non-Parametric Bootstrap
  144. 21.4.7: Distribution of Sample Means
  145. 21.4.8: Distribution of Sample Sums
  146. 22: The Appendix of Statistics
  147. 22.1: Importantly Confusing Points
  148. 22.2: Sample Statistics
  149. 22.3: Population Parameters
  150. 22.4: Probability Distributions
  151. 22.5: Distributions of Sample Statistics
  152. 22.6: Other Topics
  153. 22.7: End-of-Appendix Materials
  154. Back Matter
  155. Index
  156. Glossary
  157. Detailed Licensing
  158. Detailed Licensing