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Robust Statistics in R: Methods Resistant to Outliers and Violations
Paperback
Series: Real-World Data Science with R, Book 13
General ComputersGeneral SociologyProbability & Statistics
Publisher: Independently Published
Published: Mar 18 2026
Pages: 96
Weight: 0.31
Height: 0.20 Width: 6.00 Depth: 9.00
Language: English
ROBUST STATISTICS IN R: Methods Resistant to Outliers and Violations
In real-world data analysis, perfect datasets are rare. Outliers, noise, missing values, and violations of statistical assumptions can distort results and lead to poor decisions. This book provides a practical and reliable approach to solving these challenges using robust statistical methods in R.
Designed for data analysts, statisticians, researchers, and students, this book offers a complete guide to handling imperfect data with confidence. Instead of relying solely on traditional techniques that break under real-world conditions, readers will learn how to apply robust methods that remain stable, accurate, and trustworthy.
This book walks step-by-step through essential concepts and practical applications, including detecting and managing outliers, applying robust measures of central tendency and dispersion, and building resilient statistical models. It also covers advanced topics such as robust regression, multivariate analysis, and handling data contamination and violations of assumptions.
Each chapter combines clear explanations with hands-on R examples, allowing readers to implement techniques immediately. Practical case studies and visual illustrations help reinforce understanding and demonstrate how robust methods outperform classical approaches when dealing with messy data.
Inside this book, you will learn:
How to identify and handle outliers without losing valuable information
Robust measures such as median, MAD, trimmed means, and Winsorization
Advanced estimation techniques including M-estimators and MM-estimators
Robust regression methods such as Huber, Tukey, and quantile regression
Multivariate robust analysis including PCA and clustering
How to evaluate model performance using robust metrics
How to build a complete robust data analysis project in R
Whether you are working in finance, healthcare, business analytics, or academic research, this book equips you with the tools to handle real-world data challenges effectively.
If you want to move beyond fragile statistical methods and build models that truly reflect your data, this book provides the knowledge and practical skills you need.
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