An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

From the Publisher:
Phadke was trained in robust design techniques by Genichi Taguchi, the mastermind behind Japanese quality manufacturing technologies and the father of Japanese quality control. Taguchi's approach is currently under consideration to be adopted as a student protocol with the US govrnment. The foreword is written by Taguchi. This book offers a complete blueprint for structuring projects to achieve rapid completion with high engineering productivity during the research and development phase to ensure that high quality products can be made quickly and at the lowest possible cost. Some topics covered are: orthogonol arrays, how to construct orthogonal arrays, computer-aided robutst design techniques, dynamic systems design methods, and more.

Quality Engineering Using Robust Design

Fenton chemistry encompasses reactions of hydrogen peroxide in the presence of iron to generate highly reactive species such as the hydroxyl radical and possibly others. In this review, the complex mechanisms of Fenton and Fenton-like reactions and the important factors influencing these reactions, from both a fundamental and practical perspective, in applications to water and soil treatment, are discussed. The review covers modified versions including the photoassisted Fenton reaction, use of chelated iron, electro-Fenton reactions, and Fenton reactions using heterogeneous catalysts. Sections are devoted to nonclassical pathways, by-products, kinetics and process modeling, experimental design methodology, soil and aquifer treatment, use of Fenton in combination with other advanced oxidation processes or biodegradation, economic comparison with other advanced oxidation processes, and case studies.

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Advanced Oxidation Processes for Organic Contaminant Destruction Based on the Fenton Reaction and Related Chemistry

Alternative formulations of Levene's test statistic for equality of variances are found to be robust under nonnormality. These statistics use more robust estimators of central location in place of the mean. They are compared with the unmodified Levene's statistic, a jackknife procedure, and a χ2 test suggested by Layard which are all found to be less robust under nonnormality.

Robust Tests for the Equality of Variances

PREFACE. 1. A WORKED EXAMPLE. 1.1 A simple model. 1.2 Modulus version of the simple model. 1.3 Six--factor version of the simple model. 1.4 The simple model 'by groups'. 1.5 The (less) simple correlated--input model. 1.6 Conclusions. 2. GLOBAL SENSITIVITY ANALYSIS FOR IMPORTANCE ASSESSMENT. 2.1 Examples at a glance. 2.2 What is sensitivity analysis? 2.3 Properties of an ideal sensitivity analysis method. 2.4 Defensible settings for sensitivity analysis. 2.5 Caveats. 3. TEST CASES. 3.1 The jumping man. Applying variance--based methods. 3.2 Handling the risk of a financial portfolio: the problem of hedging. Applying Monte Carlo filtering and variance--based methods. 3.3 A model of fish population dynamics. Applying the method of Morris. 3.4 The Level E model. Radionuclide migration in the geosphere. Applying variance--based methods and Monte Carlo filtering. 3.5 Two spheres. Applying variance based methods in estimation/calibration problems. 3.6 A chemical experiment. Applying variance based methods in estimation/calibration problems. 3.7 An analytical example. Applying the method of Morris. 4. THE SCREENING EXERCISE. 4.1 Introduction. 4.2 The method of Morris. 4.3 Implementing the method. 4.4 Putting the method to work: an analytical example. 4.5 Putting the method to work: sensitivity analysis of a fish population model. 4.6 Conclusions. 5. METHODS BASED ON DECOMPOSING THE VARIANCE OF THE OUTPUT. 5.1 The settings. 5.2 Factors Prioritisation Setting. 5.3 First--order effects and interactions. 5.4 Application of Si to Setting 'Factors Prioritisation'. 5.5 More on variance decompositions. 5.6 Factors Fixing (FF) Setting. 5.7 Variance Cutting (VC) Setting. 5.8 Properties of the variance based methods. 5.9 How to compute the sensitivity indices: the case of orthogonal input. 5.9.1 A digression on the Fourier Amplitude Sensitivity Test (FAST). 5.10 How to compute the sensitivity indices: the case of non--orthogonal input. 5.11 Putting the method to work: the Level E model. 5.11.1 Case of orthogonal input factors. 5.11.2 Case of correlated input factors. 5.12 Putting the method to work: the bungee jumping model. 5.13 Caveats. 6. SENSITIVITY ANALYSIS IN DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS. 6.1 Model calibration and Factors Mapping Setting. 6.2 Monte Carlo filtering and regionalised sensitivity analysis. 6.2.1 Caveats. 6.3 Putting MC filtering and RSA to work: the problem of hedging a financial portfolio. 6.4 Putting MC filtering and RSA to work: the Level E test case. 6.5 Bayesian uncertainty estimation and global sensitivity analysis. 6.5.1 Bayesian uncertainty estimation. 6.5.2 The GLUE case. 6.5.3 Using global sensitivity analysis in the Bayesian uncertainty estimation. 6.5.4 Implementation of the method. 6.6 Putting Bayesian analysis and global SA to work: two spheres. 6.7 Putting Bayesian analysis and global SA to work: a chemical experiment. 6.7.1 Bayesian uncertainty analysis (GLUE case). 6.7.2 Global sensitivity analysis. 6.7.3 Correlation analysis. 6.7.4 Further analysis by varying temperature in the data set: fewer interactions in the model. 6.8 Caveats. 7. HOW TO USE SIMLAB. 7.1 Introduction. 7.2 How to obtain and install SIMLAB. 7.3 SIMLAB main panel. 7.4 Sample generation. 7.4.1 FAST. 7.4.2 Fixed sampling. 7.4.3 Latin hypercube sampling (LHS). 7.4.4 The method of Morris. 7.4.5 Quasi--Random LpTau. 7.4.6 Random. 7.4.7 Replicated Latin Hypercube (r--LHS). 7.4.8 The method of Sobol'. 7.4.9 How to induce dependencies in the input factors. 7.5 How to execute models. 7.6 Sensitivity analysis. 8. FAMOUS QUOTES: SENSITIVITY ANALYSIS IN THE SCIENTIFIC DISCOURSE. REFERENCES. INDEX.

Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

As part of a Bayesian analysis of the linear calibration problem, a posterior distribution is derived that is mathematically identical to the structural distribution obtained by Kalotay (1971) but different from the Bayesian distribution obtained by Hoadley (1970). Useful approximations are given. Although this posterior distribution has infinite variance, no practical problems arise in using it. The controversy concerning infinite variances and the use of inverse regression is discussed.

A Bayesian Analysis of the Linear Calibration Problem

SUMMARY When prior information is available on the parameters of a non-linear model, it should influence the choice of the appropriate experimental design. We here investigate, for the single response case, how prior information on the parameters in the form of a multivariate normal distribution can be used in selecting designs for parameter estimation. An example illustrates how the precision of the prior knowledge can affect the positioning of the design points.

The use of prior distributions in the design of experiments for parameter estimation in non-linear situations

Some experiences with the use of student projects in experimental design courses at Wisconsin are described. Each student is given the opportunity of selecting a problem of direct interest to him/her, designing and performing an experiment, collecting and analyzing the data. Some ideas with regard to pedagogy and the use of simulated data are also discussed.

Some Ideas about Teaching Design of Experiments, with 25 Examples of Experiments Conducted by Students

: When prior information is available on the parameters of a nonlinear model, it should influence the choice of the appropriate experimental design. The authors investigate, for the multiresponse case, how prior information on the parameters in the form of a multivariate normal distribution can be used in selecting designs for parameter estimation. An example illustrates how the precision of the prior knowledge can affect the positioning of the design points. (Author)

The use of prior distributions in the design of experiments for parameter estimation in non-linear situations: multiresponse case.

Los objetivos de esta nueva edicion de Estadistica para Investigadores son los mismos que los que hicieron famosa la primera edicion: poner a disposicion de los investigadores y experimentadores herramientas estadisticas y cientificas que puedan, en gran medida, catalizar la innovacion, la resolucion de problemas y los descubrimientos, y mostrar como estas herramientas pueden ser utilizadas por los especialistas en una materia a medida que avanzan sus investigaciones. Este texto servira de ayuda para todos aquellos que esten comprometidos con estos objetivos, ya sean profesores o alumnos. Gracias a la dilatada experiencia de los autores, este texto tambien proporcionara un conocimiento adecuado sobre estadistica a aquellos cientificos e ingenieros en cuyas universidades descuidaron, en el pasado, el estudio de esta importante rama de la ciencia. En esta nueva edicion, el texto original ha sido reordenado y en gran parte redactado de nuevo con el objetivo de asegurar una mayor accesibilidad a los lectores y reflejar los cambios tecnicos y tecnologicos acaecidos desde la publicacion de la primera edicion de este clasico.

William G. Hunter

Papers

A Bayesian Analysis of the Linear Calibration Problem

The use of prior distributions in the design of experiments for parameter estimation in non-linear situations

Some Ideas about Teaching Design of Experiments, with 25 Examples of Experiments Conducted by Students

The use of prior distributions in the design of experiments for parameter estimation in non-linear situations: multiresponse case.

Estadística para investigadores: diseño, innovación y descubrimiento