Multivariate analysis: Inference and hypothesis testing · Advanced statistics · Introduction

Introduction

What this guide covers

Introduction Advanced Inference Module

Overview

Topics covered

Introduction
Multivariate Analysis: Inference and Testing of Mathematical Hypotheses
Matrix structure and multivariate operator algebra
The multivariate normal distribution (MVN)
2.1 The Mahalanobis Distance
2.2 Fundamental Properties of 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{bmatrix} 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{bmatrix} = \begin{bmatrix} \mathbf{x}_1^T \\ \mathbf{x}_2^T \\ \vdots \\ \mathbf{x}_n^T \end{bmatrix}

View LaTeX source

\mathbf{X} = \begin{bmatrix}
    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{bmatrix} = \begin{bmatrix} \mathbf{x}_1^T \\ \mathbf{x}_2^T \\ \vdots \\ \mathbf{x}_n^T \end{bmatrix}