GLM - Multinomial Regression (1/3) - Intro

In this video we will look into multinomial regression, and give an introduction to the topic, including a reminder of the categorical and multinomial distributions. In the next two videos we'll go in depth to the two types of models used for nominal and ordinal response (y) data.
Alan Agresti's Book: shorturl.at/aHY79
Gordon Smyth's paper: gksmyth.github...
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“GLM in R” Course Outline:
Administration
Administration
Up to Scratch
Notebook - Introduction
Notebook - Linear Models
Notebook - Intro to R
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
Multinomial Regression
Intro
Nominal Data - Baseline Category
Ordinal Data - Cumulative Link
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[@includestdio.h8492]

Thank u so much!

[@aza6513]

Wow, will you cover nested logit model? And relationship with gev or vglm