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For a two-semester or a three-quarter calculus-based Introduction to the Mathematics of Statistics course. This classic, calculus-based introduction to the theory - and application - of statistics provides an unusually comprehensive depth and breadth of coverage and reflects the state-of-the-art in statistical thinking, the teaching of statistics, and current practices - including the use of the computer. *NEW - Places greater emphasis on the use of computers in performing statistical calculations. *NEW - Includes new exercises - many of which require the use of a computer. *NEW - Expands coverage of Analysis of Variance to include the two-way analysis-of-variance model with interaction and a discussion of multiple comparisons. *NEW - Adds appendices which summarize the properties of the special probability distributions and density functions that appear in the text. *Places greater emphasis on the use of computers in performing statistical calculations. *Comprehensive coverage of statistical theories. *Features more than 1,100 problems and exercises - divided into theory and applications.
El análisis de varianza es la técnica estadística que nos posibilita el análisis de los datos en el contexto de las investigaciones de corte experimental, en sentido amplio, lo que da cabida a estudios de corte cuasiexperimental, ex post facto. En todos los casos, nos permitirá el adecuado contraste de hipótesis y la estimación de los correspondientes parámetros. Como creemos que el manejo del soporte estadístico por parte del investigador debe ser reflexivo y crítico, es por lo que en este trabajo se trata de presentar el contenido de los diferentes capítulos con la suficiente profundidad teórica para hacer comprensibles los fundamentos lógicos y estadísticos de la técnica, sin olvidar la clarificación de la resolución práctica e informática de los distintos diseños analizados. Se incluyen en la obra las referencias precisas para hacer comprensibles los modelos subyacentes a cada una de las aplicaciones previstas (diseños de uno y dos factores, equilibrados y no equilibrados, ...), con extensión a los modelos de efectos fijos, aleatorios y mixtos. Especial atención merece el análisis y tratamiento de las condiciones subyacentes para la aplicación de los diversos modelos de ANVA.
This book is a clear and straightforward guide to analysis of variance, the backbone of experimental research. It will show you how to interpret statistical results and translate them into prose that will clearly tell your audience what your data is saying. To help you become familiar with the techniques used in analysis of variance, there are plenty of end-of-chapter practice problems with suggested answers. As life in the laboratory doesnt always follow a script, there are both new and established techniques for coping with situations that deviate from the norm. Data analysis is not a closed subject, so there are pros and cons for the varied situations you will encounter. The final chapter gives the first elementary presentation of functional measurement, or information integration theory, a methodology built upon analysis of variance that is a powerful technique for studying cognitive processes. The accompanying CD contains CALSTAT, analysis of variance software that is easy to use (really!). In addition to programs for standard analysis, the software includes several specialized routines that have heretofore been presented only in journals. Analysis of Variance is an important resource for students and professionals in the social, behavioral, and neurosciences.
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.
Systematic Glossary of the Terminology of Statistical Methods
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.