Categorical data analysis and probability distributions — advanced reference · Introduction

Introduction

What this guide covers

Theoretical foundations, multinomial distributions, Poisson models, asymptotic inference, the overdispersion problem, and rigorous algebraic derivations — A comprehensive doctoral-level reference.

Overview

Topics covered

Introduction
Categorical Data Analysis
Introduction to Categorical Data Analysis
Measurement Scales and Classification of Variables
The Probability Framework for Categorical Data
Joint, Marginal and Conditional Probabilities

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

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

\pi_{ij} = P(X = i, Y = j), \quad \text{where } \sum_{i=1}^I \sum_{j=1}^J \pi_{ij} = 1

View LaTeX source

\pi_{ij} = P(X = i, Y = j), \quad \text{where } \sum_{i=1}^I \sum_{j=1}^J \pi_{ij} = 1