Member-only story

Mastering Nonparametric Statistical Inference with John Donohue's Comprehensive Guide

John Green

·6.5k Followers· Follow

Published in Nonparametric Statistical Inference John J Donohue

5 min read · 1 month before

874 View Claps

99 Respond

Save

Listen

Nonparametric Statistical Inference

by John J. Donohue

5 out of 5

Language	:	English
File size	:	35636 KB
Screen Reader	:	Supported
Print length	:	694 pages

Nonparametric statistical inference is a powerful statistical approach that allows researchers to make inferences about a population without assuming that the data follows a specific distribution. This makes it a valuable tool for a wide range of applications, including medical research, social science research, and engineering.

John Donohue's book, Nonparametric Statistical Inference, is the definitive guide to this essential statistical approach. The book provides a comprehensive overview of the theory and practice of nonparametric inference, covering topics such as hypothesis testing, confidence intervals, and bootstrap methods.

What is Nonparametric Statistical Inference?

Nonparametric statistical inference is a statistical approach that does not assume that the data follows a specific distribution. This makes it a more flexible approach than parametric inference, which assumes that the data follows a known distribution, such as the normal distribution.

Nonparametric inference is often used when the data is not normally distributed, or when the distribution of the data is unknown. It is also used when the sample size is small, or when the data is censored.

Why is Nonparametric Statistical Inference Important?

Nonparametric statistical inference is important because it allows researchers to make inferences about a population without assuming that the data follows a specific distribution. This makes it a valuable tool for a wide range of applications, including:

Medical research: Nonparametric inference can be used to compare the effectiveness of different medical treatments, or to identify risk factors for a particular disease.
Social science research: Nonparametric inference can be used to compare the attitudes of different groups of people, or to identify trends in social behavior.
Engineering: Nonparametric inference can be used to compare the performance of different engineering designs, or to identify factors that affect the reliability of a particular system.

What is Covered in John Donohue's Book?

John Donohue's book, Nonparametric Statistical Inference, provides a comprehensive overview of the theory and practice of nonparametric inference. The book covers a wide range of topics, including:

Hypothesis testing: The book covers a variety of nonparametric hypothesis tests, including the Mann-Whitney test, the Kruskal-Wallis test, and the Kolmogorov-Smirnov test.
Confidence intervals: The book shows how to construct confidence intervals for a variety of nonparametric statistics, including the median, the mean, and the standard deviation.
Bootstrap methods: The book introduces bootstrap methods, which are a powerful tool for nonparametric inference. Bootstrap methods can be used to estimate the accuracy of a nonparametric statistic, or to construct confidence intervals.

Who Should Read John Donohue's Book?

John Donohue's book, Nonparametric Statistical Inference, is a valuable resource for anyone who wants to learn about nonparametric statistical inference. The book is written in a clear and concise style, and it provides a comprehensive overview of the theory and practice of nonparametric inference.

The book is ideal for:

Researchers who want to use nonparametric inference in their research
Students who are learning about nonparametric inference
Professionals who want to stay up-to-date on the latest developments in nonparametric inference

John Donohue's book, Nonparametric Statistical Inference, is the definitive guide to this essential statistical approach. The book provides a comprehensive overview of the theory and practice of nonparametric inference, and it is a valuable resource for anyone who wants to learn about this important statistical approach.

If you are interested in learning more about nonparametric statistical inference, I highly recommend reading John Donohue's book. The book is available from a variety of online and offline retailers.