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  • Generalized Linear Models with R

Generalized Linear Models with R

Curriculum

  • 11 Sections
  • 39 Lessons
  • Lifetime
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  • Admministration
    1
    • 1.1
      Administration
  • Up to Scratch
    4
    • 2.1
      Notebook – Introduction
    • 2.2
      Notebook – Linear Models
    • 2.3
      Notebook – Intro to R
    • 2.4
      Generalized Least Squares
  • Intro to GLMs
    4
    • 3.1
      Linear Models vs. Generalized Linear Models
    • 3.2
      Least Squares vs. Maximum Likelihood
    • 3.3
      Saturated vs. Constrained Model
    • 3.4
      Link Functions
  • Exponential Family
    5
    • 4.1
      Definition and Examples
    • 4.2
      More Examples
    • 4.3
      Notebook – Exponential Family
    • 4.4
      Mean and Variance
    • 4.5
      Notebook – Mean-Variance Relationship
  • Deviance
    2
    • 5.1
      Deviance
    • 5.2
      Notebook – Deviance
  • Likelihood Analysis
    5
    • 6.1
      Likelihood Analysis
    • 6.2
      Numerical Solution
    • 6.3
      Notebook – GLM’s in R
    • 6.4
      Notebook – Fitting the GLM
    • 6.5
      Inference
  • Code Examples
    3
    • 7.1
      Notebook – Binary/Binomial Regression
    • 7.2
      Notebook – Poisson & Negative Binomial Regression
    • 7.3
      Notebook – Gamma & Inverse Gaussian Regression
  • Advanced Topics
    5
    • 8.1
      Quasi-Likelihood
    • 8.2
      Generalized Estimating Equations – GEE
    • 8.3
      Mixed Models – GLMM
    • 8.4
      Regular vs. Mixed Models – 2 Examples
    • 8.5
      Conditional Logistic Regression
  • Multinomial Regression
    3
    • 9.1
      Introduction
    • 9.2
      Nominal Data
    • 9.3
      Ordinal Data
  • Diagnostics
    3
    • 10.1
      Linear Regression Diagnostics
    • 10.2
      GLMs Diagnostics
    • 10.3
      Multicollinearity and VIF
  • GAM
    4
    • 11.1
      GLMs vs. GAMs
    • 11.2
      Splines – Intro
    • 11.3
      Natural Cubic Splines and Smoothing Splines
    • 11.4
      Penalized Least Squares
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Multicollinearity and VIF
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Splines – Intro
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