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Nonparametric Statistics with R: When Assumptions Break Down
Paperback
Series: The Applied Data Science with R, Book 12
General ComputersGeneral SociologyProbability & Statistics
Publisher: Independently Published
Published: Mar 18 2026
Pages: 132
Weight: 0.41
Height: 0.28 Width: 6.00 Depth: 9.00
Language: English
Nonparametric Statistics with R: A Practical Guide to Rank-Based Methods, Bootstrapping, and Data Analysis When Assumptions Break Down
In real-world data analysis, assumptions rarely hold. Data is often skewed, incomplete, or filled with outliers making traditional statistical methods unreliable or misleading. This is where nonparametric statistics becomes essential.
This book provides a clear, practical, and hands-on guide to applying nonparametric methods using R, one of the most powerful tools in modern data science. Designed for students, researchers, analysts, and professionals, it bridges the gap between theory and real-world application.
Instead of relying on rigid assumptions, this book teaches you how to analyze data using flexible, robust techniques that work even when conditions are far from perfect.
Inside this book, you will learn how to:
Apply rank-based tests such as Wilcoxon, Mann-Whitney, and Kruskal-Wallis
Analyze relationships using Spearman and Kendall correlation methods
Use bootstrapping and permutation tests to estimate uncertainty
Build nonparametric regression models including LOESS and kernel smoothing
Explore and visualize data without assuming normal distribution
Handle ordinal data, skewed datasets, and outliers effectively
Implement complete nonparametric analysis workflows in R
This book stands out by combining:
Clear explanations without unnecessary complexity
Step-by-step R implementations
Practical examples and real-world scenarios
Visual illustrations and charts to enhance understanding
Whether you are working in data science, business analytics, healthcare research, or academic statistics, this guide equips you with the tools to make reliable decisions from imperfect data.
If you want to move beyond restrictive assumptions and learn how to analyze real-world data with confidence, this book is your complete roadmap.
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