Elements of Sampling Survey

R Demonstrations

Preface

Statistical inference in the real world almost always deals with bounded, finite populations—whether we are estimating agricultural yields, public health metrics, or economic indicators. This book explores the mathematical theory and practical mechanics of drawing reliable samples from those populations, examining how intelligent survey design directly dictates the precision and cost of our conclusions.

The chapters cover a comprehensive suite of methodologies, including simple random sampling, stratified random sampling, cluster and systematic sampling, probability proportionate to size (PPS) sampling, and powerful auxiliary-variable strategies like difference, ratio, and regression estimation.

Key Features

• Custom R Functions: Rather than relying exclusively on pre-built packages, this text emphasizes writing custom functions to analyze survey data, helping students learn how R works under the hood.
• Modern R Workflows: Data manipulation and presentation leverage the modern R ecosystem, utilizing dplyr for clean wrangling, ggplot2 for advanced visualization, and gt for publication-ready mathematical tables.
• Simulation-Based Learning: Extensive simulation studies demonstrate the practical differences between sampling schemes, with a core focus on how predictive auxiliary variables improve precision and reduce variance.