Experimental Design: Unified Concepts, Practical Applications, Computer Implementation is a concise and innovative book that gives a complete presentation of the design and analysis of experiments in approximately one-half the space of competing books. With only the modest prerequisite of a basic (non-calculus) statistics course this text is appropriate for the widest possible audience including college juniors, seniors and first-year graduate students in business and statistics, as well as professionals in business and industry. The book is able to accommodate this wide audience because of the unique, integrative approach that is taken to the teaching of experimental design. This text organizes and presents the two procedures for analyzing experimental design, data-analysis of variance (ANOVA) and regression analysis, in a way that allows the student to move through the material more quickly and efficiently than usual, making the true advantages of both ANOVA and regression analysis more apparent. The greater part of the book is devoted to ANOVA, the more intuitive approach to experimental design. The first three chapters are devoted to demonstrating how to use ANOVA and how to analyze the type of experimental design data that it can appropriately be used to analyze: balanced (equal sample sized) data or unbalanced (unequal sized) data from one factor studies; balanced data from two factor studies (two-way factorials and randomized block designs); and balanced data from three or more factor studies. Chapter Three includes a general ANOVA procedure for analyzing balanced data experiments
Author(s): Bowerman, Bruce L.; Murphree, Emily S.; O'Connell, Richard T
Series: 2014 digital library.; Quantitative approaches to decision making collection
Edition: First edition
Publisher: Business Expert Press
Year: 2015
Language: English
Pages: 180
Tags: Математика;Теория вероятностей и математическая статистика;Математическая статистика;Планирование эксперимента;
Content: 1. An introduction to experimental design: one factor analysis --
2. Two factor analysis --
3. More advanced experimental designs --
4. Two level factorials, fractional factorials, block confounding, and response surfaces --
Appendix A. Statistical tables --
References --
Index.