Sampling theory · Computational application and case studies · Introduction

Introduction

What this guide covers

Introduction Chart from the original guide

Overview

Introduction
Sampling Theory
Fundamentals and vocabulary of inference by sampling
1.1 · Estimators, bias and mean square error
The probabilistic framework: inclusion probabilities and the Horvitz–Thompson estimator
2.1 · Hájek's estimator for the mean

Related concepts

Compatible notation

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

\text{Total: } t_y=\sum_{i\in U} y_i, \qquad \text{Mean: } \bar{y}_U=\frac{1}{N}\sum_{i\in U} y_i,

View LaTeX source

\text{Total: } t_y=\sum_{i\in U} y_i, \qquad
  \text{Mean: } \bar{y}_U=\frac{1}{N}\sum_{i\in U} y_i,