Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.
This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation) Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process. Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages: SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter. The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods.
This book provides a reference for people working in the design, development, and manufacturing of medical devices. While there are no statistical methods specifically intended for medical devices, there are methods that are commonly applied to various problems in the design, manufacturing, and quality control of medical devices. The aim of this book is not to turn everyone working in the medical device industries into mathematical statisticians; rather, the goal is to provide some help in thinking statistically, and knowing where to go to answer some fundamental questions, such as justifying a method used to qualify/validate equipment, or what information is necessary to support the choice of sample sizes. While, there are no statistical methods specifically designed for analysis of medical device data, there are some methods that seem to appear regularly in relation to medical devices. For example, the assessment of receiver operating characteristic curves is fundamental to development of diagnostic tests, and accelerated life testing is often critical for assessing the shelf life of medical device products. Another example is sensitivity/specificity computations are necessary for in-vitro diagnostics, and Taguchi methods can be very useful for designing devices. Even notions of equivalence and noninferiority have different interpretations in the medical device field compared to pharmacokinetics. It contains topics such as dynamic modeling, machine learning methods, equivalence testing, and experimental design, for example. This book is for those with no statistical experience, as well as those with statistical knowledgeable—with the hope to provide some insight into what methods are likely to help provide rationale for choices relating to data gathering and analysis activities for medical devices.
This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation) Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process. Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages: SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter. The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods.
Statistical Methods for Field and Laboratory Studies in Behavioral Ecology focuses on how statistical methods may be used to make sense of behavioral ecology and other data. It presents fundamental concepts in statistical inference and intermediate topics such as multiple least squares regression and ANOVA. The objective is to teach students to recognize situations where various statistical methods should be used, understand the strengths and limitations of the methods, and to show how they are implemented in R code. Examples are based on research described in the literature of behavioral ecology, with data sets and analysis code provided. Features: This intermediate to advanced statistical methods text was written with the behavioral ecologist in mind Computer programs are provided, written in the R language. Datasets are also provided, mostly based, at least to some degree, on real studies. Methods and ideas discussed include multiple regression and ANOVA, logistic and Poisson regression, machine learning and model identification, time-to-event modeling, time series and stochastic modeling, game-theoretic modeling, multivariate methods, study design/sample size, and what to do when things go wrong. It is assumed that the reader has already had exposure to statistics through a first introductory course at least, and also has sufficient knowledge of R. However, some introductory material is included to aid the less initiated reader. Scott Pardo, Ph.D., is an accredited professional statistician (PStat®) by the American Statistical Association. Michael Pardo is a Ph.D. is a candidate in behavioral ecology at Cornell University, specializing in animal communication and social behavior.
Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.
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