The first step-by-step guide to conducting successful Chi-squared tests
Chi-squared testing is one of the most commonly applied statistical techniques. It provides reliable answers for researchers in a wide range of fields, including engineering, manufacturing, finance, agriculture, and medicine.
A Guide to Chi-Squared Testing brings readers up to date on recent innovations and important material previously published only in the former Soviet Union. Its clear, concise treatment and practical advice make this an ideal reference for all researchers and consultants.
Authors Priscilla E. Greenwood and Mikhail S. Nikulin demonstrate the application of these general purpose tests in a wide variety of specific settings. They also
* Detail the various decisions to be made when applying Chi-squared tests to real data, and the proper application of these tests in standard hypothesis-testing situations
* Describe how Chi-squared type tests allow statisticians to construct a test statistic whose distribution is asymptotically Chi-squared, and to compute power against various alternatives
* Devote half of the book to examples of Chi-squared tests that can be easily adapted to situations not covered in the book
* Provide a self-contained, accessible treatment of the mathematical requisites
* Include an extensive bibliography and suggestions for further reading
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From the Publisher:
Chi-squared testing is one of the most commonly applied statistical techniques, although it is regarded as an unsophisticated tool by academic statisticians. Nevertheless, it can provide useful information to researchers who have data and need reliable answers. This book aims to provide a basis for successful chi-square testing by helping the reader choose and construct the correct test. Contains practical advice for constructing chi-squared tests--a subject not typically covered in a standard statistics text/reference. Also provides an extensive bibliography.
About the Author:Priscilla E. Greenwood is Professor of Mathematics at the University of British Columbia, Vancouver, Canada. A Fellow of the Institute of Mathematical Statistics, she received her PhD in mathematics from the University of Wisconsin.
Mikhail S. Nikulin is Professor of Statistics at The University Bordeaux 2 and a member of The Laboratory of Statistical Methods of the Steklov Mathematical Institute at St. Petersburg. He earned his doctorate in the Theory of Probability and Mathematical Statistics from The Steklov Mathematical Institute in Moscow.
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