Regression Diagnostics (2/2) - Generalized Linear Models - Residuals, QQ-plot, Outliers

In this video we will look at how we can diagnose our generalized linear model fit using residuals, QQ-plots and Cook's distance. We will see how to adjust the residuals and hat-matrix of linear regression to apply also to GLMs.
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“GLM in R” Course Outline:
Administration
Administration
Up to Scratch
Notebook – Introduction
Notebook – Linear Models
Notebook – Intro to R
Generalized Least Squares
Intro to GLM’s
Linear Models vs. Generalized Linear Models
Least Squares vs. Maximum Likelihood
Saturated vs. Constrained Model
Link Functions
Exponential Family
Definition and Examples
More Examples
Notebook – Exponential Family
Mean and Variance
Notebook – Mean-Variance Relationship
Deviance
Deviance
Notebook – Deviance
Likelihood Analysis
Likelihood Analysis
Numerical Solution
Notebook – GLM’s in R
Notebook – Fitting the GLM
Inference
Code Examples:
Notebook – Binary/Binomial Regression
Notebook – Poisson & Negative Binomial Regression
Notebook – Gamma & Inverse Gaussian Regression
Advanced Topics:
Quasi-Likelihood
Generalized Estimating Equations (GEE)
Mixed Models (GLMM)
Regular vs. Mixed Models
Conditional Logistic Regression
Multinomial Regression
Intro
Nominal Data - Baseline Category
Ordinal Data - Cumulative Link
Diagnostics
Linear Regression Diagnostics
Generalized Linear Model Diagnostics
Multicollinearity and VIF
GAMs
GLMs vs. GAMs
Splines - Intro
Natural Cubic Splines and Smoothing Splines
Penalized Least Squares

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GLM (restricted) playlist: https://bit.ly/2ZMSv4U

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