Introduction To Analysis Of Variance
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Introduction to Analysis of Variance
Having trouble finding a book that shows you not only how to analyze data but also how to collect the data appropriately and fully interpret the analysis, too? Here′s a new book that does all this in a particularly readable fashion. Turner and Thayer′s text: Shows how to design an experiment in the best possible way to investigate the topic of interest Explains which associated analysis will best answer your research question Demonstrates how to conduct the analysis and then fully interpret the results in the context of your research question Organized so that the reader moves from the simplest type of design to more complex ones, the authors introduce five different kinds of ANOVA techniques and explain which design/analysis is appropriate to answer specific questions. They show how to perform each analysis using only a calculator to provide the reader with a better "feel" for the analyses than simply seeing the mathematical answers on a computer print-out. The book concludes with tips for tests on ANOVA, and descriptions of how to use the knowledge gained from the text to determine the credibility of claims made and "statistics" presented in various types of reports.
The Analysis of Variance
Author: Hardeo Sahai
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
The analysis of variance (ANOYA) models have become one of the most widely used tools of modern statistics for analyzing multifactor data. The ANOYA models provide versatile statistical tools for studying the relationship between a dependent variable and one or more independent variables. The ANOYA mod els are employed to determine whether different variables interact and which factors or factor combinations are most important. They are appealing because they provide a conceptually simple technique for investigating statistical rela tionships among different independent variables known as factors. Currently there are several texts and monographs available on the sub ject. However, some of them such as those of Scheffe (1959) and Fisher and McDonald (1978), are written for mathematically advanced readers, requiring a good background in calculus, matrix algebra, and statistical theory; whereas others such as Guenther (1964), Huitson (1971), and Dunn and Clark (1987), although they assume only a background in elementary algebra and statistics, treat the subject somewhat scantily and provide only a superficial discussion of the random and mixed effects analysis of variance.
A Student's Guide to Analysis of Variance
In the investigation of human behaviour, statistical techniques are employed widely in the social sciences. Whilst introductory statistics courses cover essential techniques, the complexities of behaviour demand that more flexible and comprehensive methods are also employed. Analysis of Variance (ANOVA) has become one of the most common of these and it is therefore essential for both student and researcher to have a thorough understanding of it. A Student's Guide to Analysis of Variance covers a range of statistical techniques associated with ANOVA, including single and multiple factor designs, various follow-up procedures such as post-hoc tests, and how to make sense of interactions. Suggestions on the best use of techniques and advice on how to avoid the pitfalls are included, along with guidelines on the writing of formal reports. Introductory level topics such as standard deviation, standard error and t-tests are revised, making this book an invaluable aid to all students for whom ANOVA is a compulsory topic. It will also serve as a useful refresher for the more advanced student and practising researcher.