Generalized linear models (GLM) · Binary and binomial responses · Introduction

Introduction

What this guide covers

Introduction Advanced Statistical Inference Module

Overview

Introduction
Generalized Linear Models: Theory and practice for binary and binomial responses
Fundamentals of Generalized Linear Models (GLM)
Modeling binary versus binomial responses
2.1 The Bernoulli case (Disaggregated Data)
2.2 The Binomial Case (Aggregated Data)

Related concepts

Compatible notation

The expression is rendered accessibly, with its LaTeX source preserved below.

f(y; \theta, \phi) = \exp\left( \frac{y\theta - b(\theta)}{a(\phi)} + c(y, \phi) \right)

View LaTeX source

f(y; \theta, \phi) = \exp\left( \frac{y\theta - b(\theta)}{a(\phi)} + c(y, \phi) \right)