Efficient factorial experiments when the data are spatially correlated

Factorial experiments with spatially arranged units occur in many situations, particularly in agricultural field trials. The design of such experiments when observations are spatially correlated is investigated in this paper. We show that having a large number of within-factor level changes in rows and columns is important for efficient and robust designs, and demonstrate how designs with these properties can be constructed. (C) 2003 Elsevier B.V. All rights reserved.

A laboratory experiment, based on the maillard reaction, conducted as a project in introductory statistics

A simple laboratory experiment, based on the Maillard reaction, served as a project in Introductory Statistics for undergraduates in Food Science and Technology. By using the principles of randomization and replication and reflecting on the sources of variation in the experimental data, students reinforced the statistical concepts and techniques introduced to them in lectures before the experiment. The experiment was run simultaneously by several student groups, using the same materials. Comparing the results of their analyses of variance, students became aware of the difference between P values and significance levels in making statistical decisions. In the experiment, the complete randomized design was applied; however, it is easy to adjust the experiment to teach students simple regression and randomized block designs.

Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

Efficient experimental designs when most treatments are unreplicated

In early generation variety trials, large numbers of new breeders' lines (varieties) may be compared, with each having little seed available. A so-called unreplicated trial has each new variety on just one plot at a site, but includes several replicated control varieties, making up around 10% and 20% of the trial. The aim of the trial is to choose some (usually around one third) good performing new varieties to go on for further testing, rather than precise estimation of their mean yields. Now that spatial analyses of data from field experiments are becoming more common, there is interest in an efficient layout of an experiment given a proposed spatial analysis and an efficiency criterion. Common optimal design criteria values depend on the usual C-matrix, which is very large, and hence it is time consuming to calculate its inverse. Since most varieties are unreplicated, the variety incidence matrix has a simple form, and some matrix manipulations can dramatically reduce the computation needed. However, there are many designs to compare, and numerical optimisation lacks insight into good design features. Some possible design criteria are discussed, and approximations to their values considered. These allow the features of efficient layouts under spatial dependence to be given and compared. (c) 2006 Elsevier Inc. All rights reserved.