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\"This is an engaging and informative book on the modern practice of experimental design.The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly.The book is a joy to read.

Dynamic models based on non-linear differential equations are increasingly being used in many biological applications. Highly informative dynamic experiments are valuable for the identification of these dynamic models. The storage of fresh fruit and vegetables is one such application where dynamic experimentation is gaining momentum. In this paper, we construct optimal O 2 and CO 2 gas input profiles to estimate the respiration and fermentation kinetics of pear fruit. The optimal input profiles, however, depend on the true values of the respiration and fermentation parameters. Locally optimal design of input profiles, which uses a single initial guess for the parameters, is the traditional method to deal with this issue. This method, however, is very sensitive to the initial values selected for the model parameters. Therefore, we present a robust experimental design approach that can handle uncertainty on the model parameters.

Fortification of food with iron is considered to be an effective approach to counter the global health problem caused by iron deficiency. However, reactivity of iron with the catechol moiety of food phenolics leads to discolouration and impairs bioavailability. In this study, we investigated the interplay between intrinsic and extrinsic factors on food discolouration caused by iron-catechol complexation. To this end, a three-level fractional factorial design was implemented. Absorbance spectra were analysed using statistical methods, including PCA, HCA, and ANOVA. Furthermore, a direct link between absorbance spectra and stoichiometry of the iron-catechol complexes was confirmed by ESI-Q-TOF-MS. All statistical methods confirm that the main effects affecting discolouration were type of iron salt, pH, and temperature. Additionally, several two-way interactions, such as type of iron salt × pH, pH × temperature, and type of iron salt × concentration significantly affected iron-catechol complexation. Our findings provide insight into iron-phenolic complexation-mediated discolouration, and facilitate the design of iron-fortified foods.

Continental export of Si to the coastal zone is closely linked to the ocean carbon sink and to the dynamics of phytoplankton blooms in coastal ecosystems. Presently, however, the impact of human cultivation of the landscape on terrestrial Si fluxes remains unquantified and is not incorporated in models for terrestrial Si mobilization. In this paper, we show that land use is the most important controlling factor of Si mobilization in temperate European watersheds, with sustained cultivation (>250 years) of formerly forested areas leading to a twofold to threefold decrease in baseflow delivery of Si. This is a breakthrough in our understanding of the biogeochemical Si cycle: it shows that human cultivation of the landscape should be recognized as an important controlling factor of terrestrial Si fluxes. Continental export of silicon to the coast is linked to ocean carbon sinks, but terrestrial silicon fluxes have not been quantified. Here, human deforestation and cultivation of the landscape are shown to be the most important factors in silicon mobilization in temperate European watersheds.

To date, no attempt has been made to design efficient choice experiments by means of the G- and V-optimality criteria. These criteria are known to make precise response predictions, which is exactly what choice experiments aim to do. In this article, the authors elaborate on the G- and V-optimality criteria for the multinomial logit model and compare their prediction performances with those of the D- and A-optimality criteria. They make use of Bayesian design methods that integrate the optimality criteria over a prior distribution of likely parameter values. They employ a modified Fedorov algorithm to generate the optimal choice designs. They also discuss other aspects of the designs, such as level overlap, utility balance, estimation performance, and computational effectiveness.

The design and analysis of experiments (DOE) has historically been an important part of an education in statistics, and with the increasing complexity of modern production processes and the advent of large-scale online experiments, it continues to be highly relevant. In this article, we provide an extensive review of the literature on DOE pedagogy, and provide five perspectives on the subject: one from each of the authors as well as a composite profile derived from a survey of DOE instructors. Our work provides a snapshot of current DOE pedagogy that showcases both the similarities and variety in how the subject is taught, as well as a look ahead at how its instruction may evolve. Supplementary materials for this article are available online.

Peter Goos, Department of Statistics, University of Leuven, Faculty of Bio-Science Engineering and University of Antwerp, Faculty of Applied Economics, Belgium David Meintrup, Department of Mathematics and Statistics, University of Applied Sciences Ingolstadt, Faculty of Mechanical Engineering, Germany Thorough presentation of introductory statistics and probability theory, with numerous examples and applications using JMP JMP: Graphs, Descriptive Statistics and Probability provides an accessible and thorough overview of the most important descriptive statistics for nominal, ordinal and quantitative data with particular attention to graphical representations. The authors distinguish their approach from many modern textbooks on descriptive statistics and probability theory by offering a combination of theoretical and mathematical depth, and clear and detailed explanations of concepts. Throughout the book, the user-friendly, interactive statistical software package JMP is used for calculations, the computation of probabilities and the creation of figures. The examples are explained in detail, and accompanied by step-by-step instructions and screenshots. The reader will therefore develop an understanding of both the statistical theory and its applications. Traditional graphs such as needle charts, histograms and pie charts are included, as well as the more modern mosaic plots, bubble plots and heat maps. The authors discuss probability theory, particularly discrete probability distributions and continuous probability densities, including the binomial and Poisson distributions, and the exponential, normal and lognormal densities. They use numerous examples throughout to illustrate these distributions and densities. Key features: * Introduces each concept with practical examples and demonstrations in JMP. * Provides the statistical theory including detailed mathematical derivations. * Presents illustrative examples in each chapter accompanied by step-by-step instructions and screenshots to help develop the reader's understanding of both the statistical theory and its applications. * A supporting website with data sets and other teaching materials. This book is equally aimed at students in engineering, economics and natural sciences who take classes in statistics as well as at masters/advanced students in applied statistics and probability theory. For teachers of applied statistics, this book provides a rich resource of course material, examples and applications.

While Bayesian - and -optimal designs for the multinomial logit model have been shown to have better predictive performance than Bayesian - and -optimal designs, the algorithms for generating them have been too slow for commercial use. In this article, we present a much faster algorithm for generating Bayesian optimal designs for all four criteria while simultaneously improving the statistical efficiency of the designs. We also show how to augment a choice design allowing for correlated parameter estimates using a sports club membership study.

We introduce a new method for generating optimal split-plot designs. These designs are optimal in the sense that they are efficient for estimating the fixed effects of the statistical model that is appropriate given the split-plot design structure. One advantage of the method is that it does not require the prior specification of a candidate set. This makes the production of split-plot designs computationally feasible in situations where the candidate set is too large to be tractable. The method allows for flexible choice of the sample size and supports inclusion of both continuous and categorical factors. The model can be any linear regression model and may include arbitrary polynomial terms in the continuous factors and interaction terms of any order. We demonstrate the usefulness of this flexibility with a 100-run polypropylene experiment involving 11 factors where we found a design that is substantially more efficient than designs that are produced by using other approaches.

Statistics with JMP: Hypothesis Tests, ANOVA and Regression  Peter Goos, University of Leuven and University of Antwerp, Belgium  David Meintrup, University of Applied Sciences Ingolstadt, Germany  A first course on basic statistical methodology using JMP This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software. Key features: * Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. * Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). * Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic. * Promotes the use of graphs and confidence intervals in addition to p-values. * Course materials and tutorials for teaching are available on the book's companion website. Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.