Description
An Introduction to Nonparametric Statistics provides a thorough foundation in nonparametric statistical methods, which are essential when data violates the assumptions required by parametric tests. This textbook covers rank-based tests, permutation tests, and distribution-free procedures that make minimal assumptions about the data’s underlying distribution.
The book progresses systematically from fundamental concepts to advanced applications, including hypothesis testing, confidence intervals, and estimation procedures. Readers will learn when and how to apply nonparametric methods to real-world problems across various fields. The text emphasizes practical implementation alongside theoretical understanding, making it accessible to students and practitioners alike.
Written by John E. Kolassa, an expert in nonparametric statistics, this Chapman & Hall/CRC publication serves as both a course text and a reference guide for applied statisticians, data analysts, and researchers seeking robust statistical alternatives.
Reviews
There are no reviews yet.