The book explores analysis of variance (ANOYA) models, essential for analyzing multifactor data in modern statistics. It simplifies the study of relationships between dependent and independent variables, highlighting variable interactions and key factors. While several texts exist, many are either too advanced or provide only superficial coverage.
This two-volume work provides a comprehensive overview of ANOVA models with random effects, essential for various fields such as agriculture, biology, and engineering. It covers point and interval estimation, and hypothesis testing, accessible to readers with basic statistical knowledge, making it suitable for students and researchers alike.
This two-volume work on ANOVA models with random effects is essential for experimental design across various fields. Volume I focuses on balanced data, covering estimation methods and hypothesis tests, while Volume II addresses unbalanced data. It's suitable for graduate students and practitioners with a basic math background.
The book delves into ANOYA models, essential tools in modern statistics for analyzing multifactor data. It highlights their effectiveness in exploring relationships between dependent and independent variables, assessing interactions, and identifying significant factors. While existing literature varies in complexity, this text aims to bridge the gap between advanced mathematical treatments and simpler, superficial discussions, offering a comprehensive yet accessible approach to random and mixed effects analysis of variance.
Volume I: Balanced Data Theory, Methods, Applications and Data Analysis
Focusing on ANOVA models with random effects, this volume provides an in-depth exploration of point estimation, interval estimation, and hypothesis testing for linear models. It specifically addresses balanced data scenarios, offering comprehensive methodologies and techniques applicable across various fields like biology and engineering. The work is structured into two volumes, with the second volume dedicated to unbalanced data models, ensuring a thorough understanding of both orthogonal and non-orthogonal approaches in experimental design.