General Linear Models - Regression

General Linear Models - Regression

statisticsmatt via YouTube Direct link

Introduction to Linear Models

1 of 70

1 of 70

Introduction to Linear Models

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General Linear Models - Regression

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  1. 1 Introduction to Linear Models
  2. 2 Simple Linear Regression
  3. 3 Simple Linear Regression: Properties of Least Squares Estimators
  4. 4 Simple Linear Regression: Estimating the Residual Variance
  5. 5 Simple Linear regression: Matrix Notation
  6. 6 Simple Linear Regression: Maximum Likelihood Estimation
  7. 7 Simple Linear Regression: Partitioning Total Variability
  8. 8 Simple Linear Regression: Matrix Notation for Sum of Squares
  9. 9 Simple Linear Regression: ANOVA Table
  10. 10 Simple Linear Regression: Testing the Model is Useful
  11. 11 Simple Linear Regression: LSEs are Normally Distributed
  12. 12 Simple Linear Regression: Confidence intervals for Beta Parameters
  13. 13 Simple Linear Regression: Coefficient of Determination
  14. 14 Simple Linear Regression:Confidence and Prediction Intervals on the Mean and Individual Response
  15. 15 Simple Linear Regression: Simultaneous Inference on B0 and B1
  16. 16 Simple Linear Regression: Bonferroni and Working-Hotelling Adjustments
  17. 17 Simple Linear Regression: Residuals and their Properties
  18. 18 Simple Linear Regression: X and Y Random
  19. 19 Simple Linear Regression: Test for the Correlation Coefficient
  20. 20 Simple Linear Regression: Fixed Zero Intercept Model
  21. 21 Multiple Linear Regression: Introduction
  22. 22 Multiple Linear Regression: Least Squares Estimates
  23. 23 Multiple Linear Regression: The Hat Matrix
  24. 24 Multiple Linear Regression: Estimating the Error Variance
  25. 25 Multiple Linear Regression: Projection and Idempotent Matrices
  26. 26 Multiple Linear Regression: Gauss Markov Theorem
  27. 27 Multiple Linear Regression: Partitioning Total Variability
  28. 28 Multiple Linear Regression: Type I Sum of Squares
  29. 29 Multiple Linear Regression: Type II Sum of Squares
  30. 30 Multiple Linear Regression: Global F Test
  31. 31 Multiple Linear Regression: Partial F Tests
  32. 32 Multiple Linear Regression: t Tests for a Single Beta Parameter
  33. 33 Multiple Linear Regression: General Linear Hypotheses
  34. 34 Using R: Simple Linear Regression from Scratch
  35. 35 Multiple Linear Regression: CI/PI on the Mean and Individual Response
  36. 36 Multiple Linear Regression: Simultaneous Inference of B'=(B0,B1, ... ,Bk)
  37. 37 Multiple Linear Regression: Partitioning the Residual Sum of Squares
  38. 38 Multiple Linear Regression: Repeated Observations and Lack of Fit Test
  39. 39 Multiple Linear Regression: Centering and Scaling the Design Matrix
  40. 40 Multiple Linear Regression: Condition Number / Multicollinearity
  41. 41 Multiple Linear Regression: Variance Inflation Factor (VIF) / Multicollinearity
  42. 42 Multiple Linear Regression: Variance Proportions / Multicollinearity
  43. 43 Multiple Linear Regression: Indicator / Dummy Variables
  44. 44 Multiple Linear Regression: AIC (Akaike Information Criterion)
  45. 45 Multiple Linear Regression: Choosing a model with R2, Adjusted R2, and MSE
  46. 46 Multiple Linear Regression: Mallow's Cp
  47. 47 Multiple Linear Regression: Impact of Under or Over Fitting a Model
  48. 48 Multiple Linear Regression: The PRESS Prediction SS Statistic
  49. 49 Multiple Linear Regression: Residual Properties
  50. 50 Weighted Least Squares Regression: Mahalanobis Distance
  51. 51 Weighted Least Squares Regression: Hat Matrix
  52. 52 Weighted Least Squares Regression: Estimability / BLUE
  53. 53 Weighted Least Squares Regression: Estimating the Error Variance
  54. 54 Weighted Least Squares Regression: Testing for Estimable Functions
  55. 55 Weighted Least Squares Regression: Partial F Tests
  56. 56 Multiple Linear Regression: Canonical Form
  57. 57 Multiple Linear Regression: Canonical Form and Multicollinearity
  58. 58 Multiple Linear Regression: Principal Components Model
  59. 59 Ridge Regression (part 1 of 4): Variance Reduction
  60. 60 Ridge Regression (part 2 of 4): Deriving the Bias
  61. 61 Ridge Regression (part 3 of 4): Deriving from 1st principles.
  62. 62 Ridge Regression (part 4 of 4): Canonical Form
  63. 63 Multiple Linear Regression: Box-Cox Transformation
  64. 64 Multiple Linear Regression: Box - Tidwell Transformation
  65. 65 Multiple Linear Regression: Studentized Residuals (Part 1 of 2)
  66. 66 Multiple Linear Regression: Studentized Residuals (Part 2 of 2)
  67. 67 Multiple Linear Regression: Partial Regression Plots (Added Variable Plots)
  68. 68 Multiple Linear Regression: Influence Measures (Part 1 of 2)
  69. 69 Multiple Linear Regression: Influence Measures (Part 2 of 2)
  70. 70 Best quadratic unbiased estimator of variance in a MLR model using Lagrange Multipliers

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