Multivariate analysis · Computational implementation and practical interpretation · Introduction

Introduction

What this guide covers

Introduction Chart from the original guide

Overview

Topics covered

Introduction
Multivariate Analysis: Implementation and Practice
Fundamentals of matrix algebra and multivariate geometry
Multivariate Metric Geometry: Mahalanobis Distance
Essential Matrix Decompositions
The Multivariate Normal Distribution

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

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

\mathbf{X} = \begin{pmatrix} x_{11} & x_{12} & \dots & x_{1p} \\ x_{21} & x_{22} & \dots & x_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ x_{n1} & x_{n2} & \dots & x_{np} \end{pmatrix} = \begin{pmatrix} \mathbf{x}_1^T \\ \mathbf{x}_2^T \\ \vdots \\ \mathbf{x}_n^T \end{pmatrix},

View LaTeX source

\mathbf{X} = \begin{pmatrix} 
      x_{11} & x_{12} & \dots & x_{1p} \\
      x_{21} & x_{22} & \dots & x_{2p} \\
      \vdots & \vdots & \ddots & \vdots \\
      x_{n1} & x_{n2} & \dots & x_{np}
    \end{pmatrix} = \begin{pmatrix} \mathbf{x}_1^T \\ \mathbf{x}_2^T \\ \vdots \\ \mathbf{x}_n^T \end{pmatrix},