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Linear Models and Rurità Kràlovstvì

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Linear Models and Rurità Kràlovstvì

Statistics
Author
Ole Forsberg
Publisher
Knox College
Source
LibreTexts
License
CC BY-NC-SA
Language
EN
Category
Basic Sciences
Read online Download PDF (open licence)
Contents· 147 sections
  1. 01: An Introduction to Ruruitania
  2. 1.01: The Background of Ruritania
  3. 1.02: The Geography
  4. 1.03: The Government
  5. 1.04: The Economy
  6. 1.05: US-Ruritanian Relations
  7. 1.06: Illustrations of Analyses
  8. 1.6.01: Illustration- The Missing Kraj
  9. 1.6.02: Illustration- Cows in Decin
  10. 1.6.03: Illustration- Ruritanian Crops
  11. 1.6.04: Illustration- Wealth in Ruritania
  12. 1.6.05: Illustration- Elections in Ruritania
  13. 1.6.06: Illustration- Insurance in Ruritania
  14. 1.6.07: Illustration- Warmth for the King
  15. 1.07: Conclusion
  16. 02: Part One- Least Squares
  17. 03: Intro to Linear Regression
  18. 3.01: Scalar Representation
  19. 3.02: First Results
  20. 3.03: Our First Assumptions
  21. 3.04: PRE Measures
  22. 3.05: Conclusion
  23. 3.06: End-of-Chapter Materials
  24. 04: Matrices and Linear Regression
  25. 4.01: Matrix Representation
  26. 4.02: Predictions and the Hat Matrix
  27. 4.03: The Geometry of OLS
  28. 4.04: Multicollinearity and Categorical Independent Variables
  29. 4.05: Conclusion
  30. 4.06: End-of-Chapter Materials
  31. 05: Improved Now with Probabilities
  32. 5.01: Probability Distributions
  33. 5.02: Test Statistics and Hypothesis Testing
  34. 5.03: Confidence Intervals
  35. 5.04: The Working-Hotelling Bands
  36. 5.05: Conclusion
  37. 5.06: End-of-Chapter Materials
  38. 06: Dood Check the Requirements
  39. 6.01: Normality
  40. 6.02: Functional Fit
  41. 6.03: Homoskedasticity
  42. 6.04: Multicollinearity
  43. 6.05: Conclusion
  44. 6.06: End-of-Chapter Materials
  45. 07: Time for Some Examples
  46. 7.01: Violent Crime
  47. 7.02: Violent Crime Wealth Region
  48. 7.03: Cows in Decin
  49. 7.04: Conclusion
  50. 7.05: End-of-Chapter Materials
  51. 08: Fixing the Violations
  52. 8.01: The Issue of Boundedness
  53. 8.02: The South Sudanese Referendum
  54. 8.03: Adjustments for Heteroskedasticity
  55. 8.04: Conclusion
  56. 8.05: End-of-Chapter Materials
  57. 09: Beyond the Ordinary
  58. 10: Other Least Squares
  59. 10.01: Ordinary Least Squares
  60. 10.02: Weighted Least Squares
  61. 10.03: Generalized Least Squares
  62. 10.04: May the (Strong) Force be with You
  63. 10.05: Elections in Ruritania
  64. 10.06: Conclusion
  65. 10.07: End-of-Chapter Materials
  66. 11: Quantile Regression
  67. 11.01: Parameter Estimation
  68. 11.02: Quantile Regression
  69. 11.03: Conclusion
  70. 11.04: End-of-Chapter Materials
  71. 12: Maximizing the Likelihood
  72. 12.01: The Likelihood
  73. 12.02: The MLE and the CLM
  74. 12.03: Conclusion
  75. 12.04: End-of-Chapter Materials
  76. 13: Beyond the Classical Model
  77. 14: Generalized Linear Models
  78. 14.01: The CLM and the GLM
  79. 14.02: The Requirements for GLMs
  80. 14.03: Assumptions of GLMs
  81. 14.04: The Gaussian Distribution
  82. 14.05: Generalized Linear Models in R
  83. 14.06: Conclusion
  84. 14.07: End-of-Chapter Materials
  85. 15: Binary Dependent Variables
  86. 15.01: Binary Dependent Variables
  87. 15.02: Latent Variable Modeling
  88. 15.03: The Mathematics
  89. 15.04: Modeling with the Logit
  90. 15.05: Measuring Accuracy
  91. 15.06: Modeling with Other Links
  92. 15.07: Model Selection
  93. 15.08: Conclusion
  94. 15.09: End-of-Chapter Material
  95. 16: Binomial Dependent Variables
  96. 16.01: Binomial Distribution
  97. 16.02: The Mathematics
  98. 16.03: O Canada
  99. 16.04: Sri Lanka in 2010
  100. 16.05: Beta-Binomial Regression
  101. 16.06: Conclusion
  102. 16.07: End-of-Chapter Materials
  103. 17: Count Dependent Variables
  104. 17.01: Linear or Poisson Regression
  105. 17.02: The Mathematics
  106. 17.03: Overdispersion
  107. 17.04: Body Counts
  108. 17.05: The Bias-Variance Tradeoff
  109. 17.06: Conclusion
  110. 17.07: End-of-Chapter Materials
  111. 18: Nominal and Ordinal Dependent Variables
  112. 18.01: Nominal Dependent Variables
  113. 18.02: Ordinal Dependent Variables
  114. 18.03: Cattle Feed
  115. 18.04: The State University of Ruritania
  116. 18.05: Conclusion
  117. 18.06: End-of-Chapter Materials
  118. 19: Appendices
  119. 20: The Appendix of Matrices
  120. 20.01: Matrix Basics
  121. 20.02: Addition
  122. 20.03: Multiplication
  123. 20.04: Other Terms and Operations
  124. 20.05: Consequences
  125. 20.06: Statistics in Matrices
  126. 20.07: End-of-Appendix Materials
  127. 21: The Appendix of R
  128. 21.01: Installing R
  129. 21.02: R Packages
  130. 21.03: R Functions
  131. 21.04: Programming Practice
  132. 21.4.01: Plotting a Distribution
  133. 21.4.02: Functions of RVs
  134. 21.4.03: Effects of Rounding
  135. 21.4.04: Algorithmic Thinking
  136. 21.4.05: Distribution of Extreme Values
  137. 21.4.06: Non-Parametric Bootstrap
  138. 21.4.07: Distribution of Sample Means
  139. 21.4.08: Distribution of Sample Sums
  140. 22: The Appendix of Statistics
  141. 22.01: Importantly Confusing Points
  142. 22.02: Sample Statistics
  143. 22.03: Population Parameters
  144. 22.04: Probability Distributions
  145. 22.05: Distributions of Sample Statistics
  146. 22.06: Other Topics
  147. 22.07: End-of-Appendix Materials