An Introduction to Statistical Inference and Its Applications

This is a book about how statisticians draw conclusions from experimen-tal data. Its primary goal is to introduce the reader to an important type of reasoning that statisticians call “statistical inference.” Rather than providea superficial introduction to a wide variety of inferential methods, we will concentrate on fundamental concepts and study a few methods in depth.

This book you having useful to all Statistics students and Research scholars.

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Non Parametric Statistics An Introduction - J. D. Gibbons

Title: Non Parametric Statistics An Introduction

Author: Jean Dickinson Gibbons

Subject: Statistics

Language: Tamil

Publisher: SAGE University Paper

ISBN:

Pages:

Book Description:

"Nonparametric Statistics is a short and sweet introduction to the five most familiar nonparametric location tests and associated confidence intervals and multiple comparisons. . . . This book is extremely limited in coverage, but none the worse for that. You won’t find anything on the Fisher Exact Test, or measures of association for the cases covered. What you will find is good, accurate, comprehensible accounts of location tests for two or more treatments. . . . The book is well-written throughout, with fewer than expected mistakes. . . . All in all, a good basic introduction to nonparametric tests, with few frills, as you would expect. The computer package comparisons sound very useful warning bells and are a welcome frill."

--British Journal of Mathematical and Statistical Psychology

Using actual research investigations that have appeared in recent social science journals, Jean Dickinson Gibbons shows you the specific methodology and logical rationale for many of the best-known and most frequently used nonparametric methods (that are applicable for most small and large sample sizes). The methods are organized according to the type of sample structure that produced the data, and the inference types covered are limited to location tests, such as the sign test, the Mann-Whitney-Wilcox on test, the Kruskal-Wallis test, and Friedman’s test. Formal introductions to each test are followed by a data example, calculated first by hand and then by computer.

Non-Parametric (Author: Gibbons)

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