An Introduction to Design, Data Analysis, and Model Building
Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for statistical thinking'' by using examples drawn from actual case studies.
Design, Innovation, and Discovery, 2nd Edition
A Classic adapted to modern timesRewritten and updated, this new edition of Statistics for Experimenters adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors' practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.Providing even greater accessibility for its users, the Second Edition is thoroughly revised and updated to reflect the changes in techniques and technologies since the publication of the classic First Edition.Among the new topics included are: Graphical Analysis of VarianceComputer Analysis of Complex DesignsSimplification by transformationHands-on experimentation using Response Service MethodsFurther development of robust product and process design using split plot arrangements and minimization of error transmissionIntroduction to Process Control, Forecasting and Time SeriesIllustrations demonstrating how multi-response problems can be solved using the concepts of active and inert factor spaces and canonical spacesBayesian approaches to model selection and sequential experimentationAn appendix featuring Quaquaversal quotes from a variety of sources including noted statisticians and scientists to famous philosophers is provided to illustrate key concepts and enliven the learning process.All the computations in the Second Edition can be done utilizing the statistical language R. Functions for displaying ANOVA and lamba plots, Bayesian screening, and model building are all included and R packages are available online. All theses topics can also be applied utilizing easy-to-use commercial software packages.Complete with applications covering the physical, engineering, biological, and social sciences, Statistics for Experimenters is designed for individuals who must use statistical approaches to conduct an experiment, but do not necessarily have formal training in statistics. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is an essential reference for all researchers and is a highly recommended course book for undergraduate and graduate students.
Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori . Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.
La nueva edición de "Estadística para Investigadores" busca ofrecer herramientas estadísticas a investigadores y experimentadores, facilitando la innovación y la resolución de problemas. Reorganizado y actualizado, el texto es ideal para profesores y estudiantes, así como para científicos e ingenieros que necesiten un conocimiento sólido en estadística.