This book should be on the shelf of every practising statistician who designs experiments. Good design considers units and treatments first, and then allocates treatments to units. It does not choose from a menu of named designs. This approach requires a notation for units that does not depend on the treatments applied. Most structure on the set of observational units, or on the set of treatments, can be defined by factors. This book develops a coherent framework for thinking about factors and their relationships, including the use of Hasse diagrams. These are used to elucidate structure, calculate degrees of freedom and allocate treatment subspaces to appropriate strata. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses. Examples, exercises and discussion questions are drawn from a wide range of real applications: from drug development, to agriculture, to manufacturing.

A valuable guide to conducting experiments and analyzing dataacross a wide range of applications Experimental design is an important component of the scientificmethod. This book provides guidance on planning efficientinvestigations. It compiles designs for a wide range ofexperimental situations not previously found in accessible form.Focusing on applications in the physical, engineering, biological,and social sciences, Planning, Construction, and StatisticalAnalysis of Comparative Experiments is a valuable guide todesigning experiments and correctly analyzing and interpreting theresults. The authors draw on their years of experience in theclassroom and as statistical consultants to research programs oncampus, in government, and in industry. The object is always tostrike the right balance between mathematical necessities andpractical constraints. Serving both as a textbook for students of intermediatestatistics and a hands-on reference for active researchers, thetext includes: A wide range of applications, including agricultural sciences,animal and biomedical sciences, and industrial engineeringstudies General formulas for estimation and hypothesis testing,presented in a unified and simplified manner Guidelines for evaluating the power and efficiency of designsthat are not perfectly balanced New developments in the design of fractional factorials withnon-prime numbers of levels in mixed-level fractionalfactorials Detailed coverage on the construction of plans and therelationship among categories of designs Thorough coverage of balanced, lattice, cyclic, and alphadesigns Strategies for sequences of fractional factorials Data sets and SAS® code on a companion web site An ideal handbook for the investigator planning a researchprogram, the text comes complete with detailed plans of experimentsand alternative approaches for added flexibility.

This book should be on the shelf of every practising statistician who designs experiments. Good design considers units and treatments first, and then allocates treatments to units. It does not choose from a menu of named designs. This approach requires a notation for units that does not depend on the treatments applied. Most structure on the set of observational units, or on the set of treatments, can be defined by factors. This book develops a coherent framework for thinking about factors and their relationships, including the use of Hasse diagrams. These are used to elucidate structure, calculate degrees of freedom and allocate treatment subspaces to appropriate strata. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses. Examples, exercises and discussion questions are drawn from a wide range of real applications: from drug development, to agriculture, to manufacturing.

Comparative Experiments with Field Crops is a guide on how to organize comparative experiments about field crops. The book is divided into two parts. Part I deals with how to do and interpret field experiments; the planning of field experiments and special considerations to take into mind; the marking out, sowing, counting, and scoring of field crops; and the harvest as well as sampling. Part II covers the statistics involved in field experiments, and include comparisons and degrees of freedom and error; multidimensional geometry; regression analysis; and analysis of covariance. The text is recommended for agriculturists and botanists who intend to make a comparative study on field crops and are in need of a reference.

This volume describes practical techniques for conducting field experiments. The style is relaxed and enjoyable to read, with new chapters concerning difficult sites; how to arrange tests that can help solve farmers' immediate problems; and patterns of fertility and their study in statistical analysis by numerical and graphical methods. The emphasis is on understanding statistics without the use of detailed mathematical formulae; researchers with little statistical training and scientists planning trials will find this work invaluable.

This edition includes increased emphasis on the connection between the experiment and the model that can be developed from the results of the experiment, as well as material on computer output and computer graphics.

The information in an experiment; Linear statistical models; Some noise-reducing experimental designs; An example of a volume increasing design; Fitting the general linear model; Inference making; Multiparameter hypotheses: the analysis of variance; The effect of coding on the analysis; Seeking a maximum or minimum response; Fractional factorial experiments and incomplete block designs; An example of a completely random model; Mixed models.