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| computer intensive methods for testing hypotheses: Computer-Intensive Methods for Testing Hypotheses Eric W. Noreen, 1989-05-02 How to use computer-intensive methods to assess the significance of a statistic in an hypothesis test--for both statisticians and nonstatisticians alike. The significance of almost any test can be assessed using one of the methods presented here, for the techniques given are very general (e.g. virtually every nonparametric statistical test is a special case of one of the methods covered). Programs presented are brief, easy to read, require minimal programming, and can be run on most PC's. They also serve as templates adaptable to a wide range of applications. Includes numerous illustrations of how to apply computer-intensive methods. |
| computer intensive methods for testing hypotheses: Computer-Intensive Methods for Testing Hypotheses Eric W. Noreen, 1989-05-02 How to use computer-intensive methods to assess the significance of a statistic in an hypothesis test--for both statisticians and nonstatisticians alike. The significance of almost any test can be assessed using one of the methods presented here, for the techniques given are very general (e.g. virtually every nonparametric statistical test is a special case of one of the methods covered). Programs presented are brief, easy to read, require minimal programming, and can be run on most PC's. They also serve as templates adaptable to a wide range of applications. Includes numerous illustrations of how to apply computer-intensive methods. |
| computer intensive methods for testing hypotheses: CMT Curriculum Level III 2022 CMT Association, 2021-12-14 Get Your Copy of the Official 2022 CMT® Level III Curriculum Building upon the concepts covered in Levels I and II, the Official CMT® Level III Curriculum is the authoritative resource for all candidates preparing for their final CMT exam in June or December of 2022. This text explores asset relationships, portfolio management, behavioral finance, volatility analysis, and more. Published in partnership with the CMT Association, CMT Curriculum Level III 2022: The Integration of Technical Analysis covers all concepts featured on the Level III CMT® exam, and is designed to improve candidates’ understanding of key topics in the theory and analysis of markets and securities. |
| computer intensive methods for testing hypotheses: Randomization, Bootstrap and Monte Carlo Methods in Biology Bryan F.J. Manly, 2018-10-03 Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates the value of a number of these methods with an emphasis on biological applications. This textbook focuses on three related areas in computational statistics: randomization, bootstrapping, and Monte Carlo methods of inference. The author emphasizes the sampling approach within randomization testing and confidence intervals. Similar to randomization, the book shows how bootstrapping, or resampling, can be used for confidence intervals and tests of significance. It also explores how to use Monte Carlo methods to test hypotheses and construct confidence intervals. New to the Third Edition Updated information on regression and time series analysis, multivariate methods, survival and growth data as well as software for computational statistics References that reflect recent developments in methodology and computing techniques Additional references on new applications of computer-intensive methods in biology Providing comprehensive coverage of computer-intensive applications while also offering data sets online, Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition supplies a solid foundation for the ever-expanding field of statistics and quantitative analysis in biology. |
| computer intensive methods for testing hypotheses: CMT Level III 2020 Wiley, 2020-01-02 Everything you need to pass Level III of the CMT Program CMT Level III 2020: The Integration of Technical Analysis fully prepares you to demonstrate competency integrating basic concepts in Level I with practical applications in Level II, by using critical analysis to arrive at well-supported, ethical investing and trading recommendations. Covered topics include: asset relationships, portfolio management, behavioral finance, volatility, and analysis. The Level III exam emphasizes risk management concepts as well as classical methods of technical analysis. This cornerstone guidebook of the Chartered Market Technician® Program will provide every advantage to passing Level III CMT Exam. |
| computer intensive methods for testing hypotheses: CMT Curriculum Level III 2023 CMT Association, 2022-12-28 Get Your Copy of the Official 2023 CMT® Level III Curriculum Building upon the concepts covered in Levels I and II, the Official CMT® Level III Curriculum is the authoritative resource for all candidates preparing for their final CMT exam in June or December of 2023. This text explores asset relationships, portfolio management, behavioral finance, volatility analysis, and more. Published in partnership with the CMT Association, CMT Curriculum Level III 2023: The Integration of Technical Analysis covers all concepts featured on the Level III CMT® exam, and is designed to improve candidates' understanding of key topics in the theory and analysis of markets and securities. |
| computer intensive methods for testing hypotheses: CMT Level III 2019 Wiley, 2018-12-27 Everything you need to pass Level III of the CMT Program CMT Level III 2019: The Integration of Technical Analysis fully prepares you to demonstrate competency integrating basic concepts in Level I with practical applications in Level II, by using critical analysis to arrive at well-supported, ethical investing and trading recommendations. Covered topics include: asset relationships, portfolio management, behavioral finance, volatility, and analysis. The Level III exam emphasizes risk management concepts as well as classical methods of technical analysis. This cornerstone guidebook of the Chartered Market Technician® Program will provide every advantage to passing Level III CMT Exam. |
| computer intensive methods for testing hypotheses: Abdominal Imaging: Computational and Clinical Applications Hiroyuki Yoshida, Georgios Sakas, Marius George Linguraru, 2012-03-06 This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on Computational and Clinical Applications in Abdominal Imaging, held in conjunction with MICCAI 2011, in Toronto, Canada, on September 18, 2011. The 33 revised full papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections on virtual colonoscopy and CAD, abdominal intervention, and computational abdominal anatomy. |
| computer intensive methods for testing hypotheses: Validity, Reliability, and Significance Stefan Riezler, Michael Hagmann, 2022-06-01 Empirical methods are means to answering methodological questions of empirical sciences by statistical techniques. The methodological questions addressed in this book include the problems of validity, reliability, and significance. In the case of machine learning, these correspond to the questions of whether a model predicts what it purports to predict, whether a model's performance is consistent across replications, and whether a performance difference between two models is due to chance, respectively. The goal of this book is to answer these questions by concrete statistical tests that can be applied to assess validity, reliability, and significance of data annotation and machine learning prediction in the fields of NLP and data science. Our focus is on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs). Based on the interpretable parameters of the trained GAMs or LMEMs, the book presents model-based statistical tests such as a validity test that allows detecting circular features that circumvent learning. Furthermore, the book discusses a reliability coefficient using variance decomposition based on random effect parameters of LMEMs. Last, a significance test based on the likelihood ratio of nested LMEMs trained on the performance scores of two machine learning models is shown to naturally allow the inclusion of variations in meta-parameter settings into hypothesis testing, and further facilitates a refined system comparison conditional on properties of input data. This book can be used as an introduction to empirical methods for machine learning in general, with a special focus on applications in NLP and data science. The book is self-contained, with an appendix on the mathematical background on GAMs and LMEMs, and with an accompanying webpage including R code to replicate experiments presented in the book. |
| computer intensive methods for testing hypotheses: Introduction to Environmental Toxicology Wayne Landis, Ruth Sofield, Ming-Ho Yu, 2017-09-29 The fifth edition includes new sections on the use of adverse outcome pathways, how climate change changes how we think about toxicology, and a new chapter on contaminants of emerging concern. Additional information is provided on the derivation of exposure-response curves to describe toxicity and they are compared to the use of hypothesis testing. The text is unified around the theme of describing the entire cause-effect pathway from the importance of chemical structure in determining exposure and interaction with receptors to the use of complex systems and hierarchical patch dynamic theory to describe effects to landscapes. |
| computer intensive methods for testing hypotheses: Latent Class Analysis of Survey Error Paul P. Biemer, 2011-03-16 Combining theoretical, methodological, and practical aspects, Latent Class Analysis of Survey Error successfully guides readers through the accurate interpretation of survey results for quality evaluation and improvement. This book is a comprehensive resource on the key statistical tools and techniques employed during the modeling and estimation of classification errors, featuring a special focus on both latent class analysis (LCA) techniques and models for categorical data from complex sample surveys. Drawing from his extensive experience in the field of survey methodology, the author examines early models for survey measurement error and identifies their similarities and differences as well as their strengths and weaknesses. Subsequent chapters treat topics related to modeling, estimating, and reducing errors in surveys, including: Measurement error modeling forcategorical data The Hui-Walter model and othermethods for two indicators The EM algorithm and its role in latentclass model parameter estimation Latent class models for three ormore indicators Techniques for interpretation of modelparameter estimates Advanced topics in LCA, including sparse data, boundary values, unidentifiability, and local maxima Special considerations for analyzing datafrom clustered and unequal probability samples with nonresponse The current state of LCA and MLCA (multilevel latent class analysis), and an insightful discussion on areas for further research Throughout the book, more than 100 real-world examples describe the presented methods in detail, and readers are guided through the use of lEM software to replicate the presented analyses. Appendices supply a primer on categorical data analysis, and a related Web site houses the lEM software. Extensively class-tested to ensure an accessible presentation, Latent Class Analysis of Survey Error is an excellent book for courses on measurement error and survey methodology at the graduate level. The book also serves as a valuable reference for researchers and practitioners working in business, government, and the social sciences who develop, implement, or evaluate surveys. |
| computer intensive methods for testing hypotheses: An Introduction to Statistical Concepts Richard G Lomax, Debbie L. Hahs-Vaughn, 2013-06-19 This comprehensive, flexible text is used in both one- and two-semester courses to review introductory through intermediate statistics. Instructors select the topics that are most appropriate for their course. Its conceptual approach helps students more easily understand the concepts and interpret SPSS and research results. Key concepts are simply stated and occasionally reintroduced and related to one another for reinforcement. Numerous examples demonstrate their relevance. This edition features more explanation to increase understanding of the concepts. Only crucial equations are included. In addition to updating throughout, the new edition features: New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award. A new chapter on logistic regression models for today's more complex methodologies. More on computing confidence intervals and conducting power analyses using G*Power. Many more SPSS screenshots to assist with understanding how to navigate SPSS and annotated SPSS output to assist in the interpretation of results. Extended sections on how to write-up statistical results in APA format. New learning tools including chapter-opening vignettes, outlines, and a list of key concepts, many more examples, tables, and figures, boxes, and chapter summaries. More tables of assumptions and the effects of their violation including how to test them in SPSS. 33% new conceptual, computational, and all new interpretative problems. A website that features PowerPoint slides, answers to the even-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets that can be used in SPSS and other packages, and more. Each chapter begins with an outline, a list of key concepts, and a vignette related to those concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides instructions for how to run SPSS, including annotated output, and tips to develop an APA style write-up. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. 'Stop and Think' boxes provide helpful tips for better understanding the concepts. Each chapter includes computational, conceptual, and interpretive problems. The data sets used in the examples and problems are provided on the web. Answers to the odd-numbered problems are given in the book. The first five chapters review descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution, and probability and sampling. The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance and regression models. Topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple and logistic regression models are also reviewed. Intended for one- or two-semester courses in statistics taught in education and/or the behavioral sciences at the graduate and/or advanced undergraduate level, knowledge of statistics is not a prerequisite. A rudimentary knowledge of algebra is required. |
| computer intensive methods for testing hypotheses: Metrological Infrastructure Beat Jeckelmann, Robert Edelmaier, 2023-07-24 Metrology is part of the essential but largely hidden infrastructure of the modern world. This book concentrates on the infrastructure aspects of metrology. It introduces the underlying concepts: International system of units, traceability and uncertainty; and describes the concepts that are implemented to assure the comparability, reliability and quantifiable trust of measurement results. It is shown what benefits the traditional metrological principles have in fields as medicine or in the evaluation of cyber physical systems. |
| computer intensive methods for testing hypotheses: Elements of Computational Statistics James E. Gentle, 2006-04-18 Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books |
| computer intensive methods for testing hypotheses: Mechanical Reliability Improvement Robert Little, 2002-09-25 Providing probability and statistical concepts developed using pseudorandom numbers, this book covers enumeration-, simulation-, and randomization-based statistical analyses for comparison of the test performance of alternative designs, as well as simulation- and randomization-based tests for examination of the credibility of statistical presumptions. the book discusses centroid and moment of inertia analogies for mean and variance and the organization structure of completely randomized, randomized complete block, and split spot experiment test programs. Purchase of the text provides access to 200 microcomputer programs illustrating a wide range of reliability and statistical analyses. |
| computer intensive methods for testing hypotheses: Structural Equation Modeling With AMOS Barbara M. Byrne, 2016-06-10 This bestselling text provides a practical guide to structural equation modeling (SEM) using the Amos Graphical approach. Using clear, everyday language, the text is ideal for those with little to no exposure to either SEM or Amos. The author reviews SEM applications based on actual data taken from her own research. Each chapter walks readers through the steps involved (specification, estimation, evaluation, and post hoc modification) in testing a variety of SEM models. Accompanying each application is: an explanation of the issues addressed and a schematic presentation of hypothesized model structure; Amos input and output with interpretations; use of the Amos toolbar icons and pull-down menus; and data upon which the model application was based, together with updated references pertinent to the SEM model tested. Thoroughly updated throughout, the new edition features: All new screen shots featuring Amos Version 23. Descriptions and illustrations of Amos’ new Tables View format which enables the specification of a structural model in spreadsheet form. Key concepts and/or techniques that introduce each chapter. Alternative approaches to model analyses when enabled by Amos thereby allowing users to determine the method best suited to their data. Provides analysis of the same model based on continuous and categorical data (Ch. 5) thereby enabling readers to observe two ways of specifying and testing the same model as well as compare results. All applications based on the Amos graphical mode interface accompanied by more how to coverage of graphical techniques unique to Amos. More explanation of key procedures and analyses that address questions posed by readers All application data files are available at www.routledge.com/9781138797031. The two introductory chapters in Section 1 review the fundamental concepts of SEM methodology and a general overview of the Amos program. Section 2 provides single-group analyses applications including two first-order confirmatory factor analytic (CFA) models, one second-order CFA model, and one full latent variable model. Section 3 presents multiple-group analyses applications with two rooted in the analysis of covariance structures and one in the analysis of mean and covariance structures. Two models that are increasingly popular with SEM practitioners, construct validity and testing change over time using the latent growth curve, are presented in Section 4. The book concludes with a review of the use of bootstrapping to address non-normal data and a review of missing (or incomplete) data in Section 5. An ideal supplement for graduate level courses in psychology, education, business, and social and health sciences that cover the fundamentals of SEM with a focus on Amos, this practical text continues to be a favorite of both researchers and practitioners. A prerequisite of basic statistics through regression analysis is recommended but no exposure to either SEM or Amos is required. |
| computer intensive methods for testing hypotheses: Statistical Modeling and Analysis for Database Marketing Bruce Ratner, 2003-05-28 Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis fo |
| computer intensive methods for testing hypotheses: Statistical and Machine-Learning Data Mining: Bruce Ratner, 2017-07-12 Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with. |
| computer intensive methods for testing hypotheses: Inductive Logic Programming Stan Matwin, 2003-02-12 This book constitutes the thoroughly refereed post-proceedings of the 12th International Conference on Inductive Logic Programming, ILP 2002, held in Sydney, Australia in July 2002. The 22 revised full papers presented were carefully selected during two rounds of reviewing and revision from 45 submissions. Among the topics addressed are first order decision lists, learning with description logics, bagging in ILP, kernel methods, concept learning, relational learners, description logic programs, Bayesian classifiers, knowledge discovery, data mining, logical sequences, theory learning, stochastic logic programs, machine discovery, and relational pattern discovery. |
| computer intensive methods for testing hypotheses: Modeling Biological Systems: James W. Haefner, 2005-12-05 I Principles 1 1 Models of Systems 3 1. 1 Systems. Models. and Modeling . . . . . . . . . . . . . . . . . . . . 3 1. 2 Uses of Scientific Models . . . . . . . . . . . . . . . . . . . . . . . . 4 1. 3 Example: Island Biogeography . . . . . . . . . . . . . . . . . . . . . 6 1. 4 Classifications of Models . . . . . . . . . . . . . . . . . . . . . . . . 10 1. 5 Constraints on Model Structure . . . . . . . . . . . . . . . . . . . . . 12 1. 6 Some Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1. 7 Misuses of Models: The Dark Side . . . . . . . . . . . . . . . . . . . 13 1. 8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 The Modeling Process 17 2. 1 Models Are Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2. 2 Two Alternative Approaches . . . . . . . . . . . . . . . . . . . . . . 18 2. 3 An Example: Population Doubling Time . . . . . . . . . . . . . . . . 24 2. 4 Model Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2. 5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3 Qualitative Model Formulation 32 3. 1 How to Eat an Elephant . . . . . . . . . . . . . . . . . . . . . . . . . 32 3. 2 Forrester Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3. 3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3. 4 Errors in Forrester Diagrams . . . . . . . . . . . . . . . . . . . . . . 44 3. 5 Advantages and Disadvantages of Forrester Diagrams . . . . . . . . . 44 3. 6 Principles of Qualitative Formulation . . . . . . . . . . . . . . . . . . 45 3. 7 Model Simplification . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3. 8 Other Modeling Problems . . . . . . . . . . . . . . . . . . . . . . . . 49 viii Contents . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. 9 Exercises 53 4 Quantitative Model Formulation: I 4. 1 From Qualitative to Quantitative . . . . . . . . . . . . . . . . . Finite Difference Equations and Differential Equations 4. 2 . . . . . . . . . . . . . . . . 4. 3 Biological Feedback in Quantitative Models . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 4 Example Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 5 Exercises 5 Quantitative Model Formulation: I1 81 . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 Physical Processes 81 . . . . . . . . . . . . . . . 5. 2 Using the Toolbox of Biological Processes 89 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3 Useful Functions 96 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 4 Examples 102 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 5 Exercises 104 6 Numerical Techniques 107 . . . . . . . . . . . . . . . . . . . . . . . 6. 1 Mistakes Computers Make 107 . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 2 Numerical Integration 110 . . . . . . . . . . . . . . . . 6. 3 Numerical Instability and Stiff Equations 115 . . . . . . . . . . . . . . |
| computer intensive methods for testing hypotheses: Crossroads between Contrastive Linguistics, Translation Studies and Machine Translation Oliver Czulo, Silvia Hansen-Schirra, 2017 Contrastive Linguistics (CL), Translation Studies (TS) and Machine Translation (MT) have common grounds: They all work at the crossroad where two or more languages meet. Despite their inherent relatedness, methodological exchange between the three disciplines is rare. This special issue touches upon areas where the three fields converge. It results directly from a workshop at the 2011 German Association for Language Technology and Computational Linguistics (GSCL) conference in Hamburg where researchers from the three fields presented and discussed their interdisciplinary work. While the studies contained in this volume draw from a wide variety of objectives and methods, and various areas of overlaps between CL, TS and MT are addressed, the volume is by no means exhaustive with regard to this topic. Further cross-fertilisation is not only desirable, but almost mandatory in order to tackle future tasks and endeavours.} |
| computer intensive methods for testing hypotheses: Database Issues for Data Visualization John P. Lee, 1994-10-05 This volume presents the proceedings of the International Workshop on Database Issues for Data Visualization, held in conjunction with the IEEE Visualization '93 conference in San Jose, California in October 1993. The book contains 13 technical contributions organized in sections on datamodels; system integration issues; and interaction, user interfaces, and presentation issues. In addition there are three introductory section surveys and an overall workshop description summarizing the whole event. In total, the reader is presented with a thoroughly refereed and carefully edited state-of-the-art report on the hot interdisciplinary topic of database issues and data visualization. |
| computer intensive methods for testing hypotheses: Introduction to Statistical Mediation Analysis David Peter MacKinnon, 2008 First Published in 2007. Routledge is an imprint of Taylor & Francis, an informa company. |
| computer intensive methods for testing hypotheses: Analyzing Social Networks Using R Stephen P. Borgatti, Martin G. Everett, Jeffrey C. Johnson, Filip Agneessens, 2022-04-21 This approachable book introduces network research in R, walking you through every step of doing social network analysis. Drawing together research design, data collection and data analysis, it explains the core concepts of network analysis in a non-technical way. The book balances an easy to follow explanation of the theoretical and statistical foundations underpinning network analysis with practical guidance on key steps like data management, preparation and visualisation. With clarity and expert insight, it: • Discusses measures and techniques for analyzing social network data, including digital media • Explains a range of statistical models including QAP and ERGM, giving you the tools to approach different types of networks • Offers digital resources like practice datasets and worked examples that help you get to grips with R software |
| computer intensive methods for testing hypotheses: Bootstrapping Christopher Z. Mooney, Robert D. Duval, Robert Duvall, 1993-08-09 This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . . --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages. |
| computer intensive methods for testing hypotheses: Counteracting Methodological Errors in Behavioral Research Gideon J. Mellenbergh, 2019-05-16 This book describes methods to prevent avoidable errors and to correct unavoidable ones within the behavioral sciences. A distinguishing feature of this work is that it is accessible to students and researchers of substantive fields of the behavioral sciences and related fields (e.g., health sciences and social sciences). Discussed are methods for errors that come from human and other factors, and methods for errors within each of the aspects of empirical studies. This book focuses on how empirical research is threatened by different types of error, and how the behavioral sciences in particular are vulnerable due to the study of human behavior and human participation in studies. Methods to counteract errors are discussed in depth including how they can be applied in all aspects of empirical studies: sampling of participants, design and implementation of the study, instrumentation and operationalization of theoretical variables, analysis of the data, and reporting of the study results. Students and researchers of methodology, psychology, education, and statistics will find this book to be particularly valuable. Methodologists can use the book to advice clients on methodological issues of substantive research. |
| computer intensive methods for testing hypotheses: The Jackknife and Bootstrap Jun Shao, Dongsheng Tu, 2012-12-06 The jackknife and bootstrap are the most popular data-resampling meth ods used in statistical analysis. The resampling methods replace theoreti cal derivations required in applying traditional methods (such as substitu tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples. Because of the availability of inexpensive and fast computing, these computer-intensive methods have caught on very rapidly in recent years and are particularly appreciated by applied statisticians. The primary aims of this book are (1) to provide a systematic introduction to the theory of the jackknife, the bootstrap, and other resampling methods developed in the last twenty years; (2) to provide a guide for applied statisticians: practitioners often use (or misuse) the resampling methods in situations where no theoretical confirmation has been made; and (3) to stimulate the use of the jackknife and bootstrap and further devel opments of the resampling methods. The theoretical properties of the jackknife and bootstrap methods are studied in this book in an asymptotic framework. Theorems are illustrated by examples. Finite sample properties of the jackknife and bootstrap are mostly investigated by examples and/or empirical simulation studies. In addition to the theory for the jackknife and bootstrap methods in problems with independent and identically distributed (Li.d.) data, we try to cover, as much as we can, the applications of the jackknife and bootstrap in various complicated non-Li.d. data problems. |
| computer intensive methods for testing hypotheses: Genes, Fossils, and Behaviour Peter Donnelly, Robert Foley, 2001 While the basic pattern of hominid evolution is well documented, the recent evolutionary history of homo sapiens is less clear. Application of molecular genetics techniques has great potential for resolving issues over this period, but as the complexity of such data increases, the quantitative methods used for its analysis are becoming more important. This phase is also one of the richest for biological and behavioural evidence derived from both fossils and archaeology. The book will contain expository and state-of-the-art research contributions from experts in these diverse areas, covering data and its interpretation, and experimental and analytical techniques. |
| computer intensive methods for testing hypotheses: Risk Assessment Michael C. Newman, Carl Strojan, 1998-05-01 Accurate risk assessments are vital to the protection of human, environmental, and ecosystem health. Risk Assessment provides a current, comprehensive reference for researchers and professionals concerned with environmental contamination as well as its effects on humans and ecosystems. |
| computer intensive methods for testing hypotheses: The Design and Analysis of Research Studies Bryan F. J. Manly, 1992-05-14 This book provides research workers with the statistical background needed in order to collect and analyze data in an intelligent and critical manner. Key examples and case studies are used to illustrate commonly encountered research problems and to explain how they may be solved or even avoided altogether. Professor Manly also presents a clear understanding of the opportunities and limitations of different research designs, as well as an introduction to some new methods of analysis that are proving increasingly popular. Topics covered include: the differences between observational and experimental studies, the design of sample surveys, multiple regression, interrupted time series, computer intensive statistics, and the ethical considerations of research. In the final chapter, there is a discussion of how the various components of a research study come together. |
| computer intensive methods for testing hypotheses: Metrology: from Physics Fundamentals to Quality of Life P. Tavella, M.J.T. Milton, M. Inguscio, 2018-01-03 Metrology is a constantly evolving field, and one which has developed in many ways in the last four decades. This book presents the proceedings of the Enrico Fermi Summer School on the topic of Metrology, held in Varenna, Italy, from 26 June to 6 July 2017. This was the 6th Enrico Fermi summer school devoted to metrology, the first having been held in 1976. The 2017 program addressed two major new directions for metrology: the work done in preparation for a possible re-definition of four of the base units of the SI in 2018, and the impact of the application of metrology to issues addressing quality of life – such as global climate change and clinical and food analysis – on science, citizens and society. The lectures were grouped into three modules: metrology for quality of life; fundamentals of metrology; and physical metrology and fundamental constants, and topics covered included food supply and safety; biomarkers; monitoring climate and air quality; new IS units; measurement uncertainty; fundamental constants; electrical metrology; optical frequency standards; and photometry and light metrology. The book provides an overview of the topics and changes relevant to metrology today, and will be of interest to both academics and all those whose work involves any of the various aspects of this field. |
| computer intensive methods for testing hypotheses: Structure Discovery in Natural Language Chris Biemann, 2011-12-08 Current language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet. This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process? After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction. The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems. |
| computer intensive methods for testing hypotheses: Natural Language Processing and Information Systems Rafael Munoz, Andres Montoyo, Elisabeth Metais, 2011-06-16 This book constitutes the refereed proceedings of the 16th International Conference on Applications of Natural Language to Information Systems, held in Alicante, Spain, in June 2011. The 11 revised full papers and 11 revised short papers presented together with 23 poster papers, 1 invited talk and 6 papers of the NLDB 2011 doctoral symposium were carefully reviewed and selected from 74 submissions. The papers address all aspects of Natural Language Processing related areas and present current research on topics such as natural language in conceptual modeling, NL interfaces for data base querying/retrieval, NL-based integration of systems, large-scale online linguistic resources, applications of computational linguistics in information systems, management of textual databases NL on data warehouses and data mining, NLP applications, as well as NL and ubiquitous computing. |
| computer intensive methods for testing hypotheses: Design of Water Quality Monitoring Systems Robert C. Ward, Jim C. Loftis, Graham B. McBride, 1991-01-16 Design of Water Quality Monitoring Systems Design of Water Quality Monitoring Systems presents a state-of-the-art approach to designing a water quality monitoring system that gets consistently valid results. It seeks to provide a strong scientific basis for monitoring that will enable readers to establish cost-effective environmental programs. The book begins by reviewing the evolution of water quality monitoring as an information system, and then defines water quality monitoring as a system, following the flow of information through six major components: sample collection, laboratory analysis, data handling, data analysis, reporting, and information utilization. The importance of statistics in obtaining useful information is discussed next, followed by the presentation of an overall approach to designing a total water quality information system. This sets the stage for a thorough examination of the quantification of information expectations, data analysis, network design, and the writing of the final design report. Several case studies describe the efforts of various organizations and individuals to design water quality monitoring systems using many of the concepts discussed here. A helpful summary and final system design checklist are also provided. Design of Water Quality Monitoring Systems will be an essential working tool for a broad range of managers, environmental scientists, chemists, toxicologists, regulators, and public officials involved in monitoring water quality. The volume will also be of great interest to professionals in government, industry, and academia concerned with establishing sound environmental programs. |
| computer intensive methods for testing hypotheses: A Guide to Econometrics Peter Kennedy, 2008-02-19 This is the perfect (and essential) supplement for all econometrics classes--from a rigorous first undergraduate course, to a first master's, to a PhD course. Explains what is going on in textbooks full of proofs and formulas Offers intuition, skepticism, insights, humor, and practical advice (dos and don’ts) Contains new chapters that cover instrumental variables and computational considerations Includes additional information on GMM, nonparametrics, and an introduction to wavelets |
| computer intensive methods for testing hypotheses: Pediatric Biomedical Informatics John J. Hutton, 2012-12-13 Advances in the biomedical sciences, especially genomics, proteomics, and metabolomics, taken together with the expanding use of electronic health records, are radically changing the IT infrastructure and software applications needed to support the transfer of knowledge from bench to bedside. Pediatric Biomedical Informatics: Computer Applications in Pediatric Research describes the core resources in informatics necessary to support biomedical research programs and how these can best be integrated with hospital systems to receive clinical information that is necessary to conduct translational research.The focus is on the authors’ recent practical experiences in establishing an informatics infrastructure in a large research-intensive children’s hospital. This book is intended for translational researchers and informaticians in pediatrics, but can also serve as a guide to all institutions facing the challenges of developing and strengthening informatics support for biomedical research. The first section of the book discusses important technical challenges underlying computer-based pediatric research, while subsequent sections discuss informatics applications that support biobanking and a broad range of research programs. Pediatric Biomedical Informatics provides practical insights into the design, implementation, and utilization of informatics infrastructures to optimize care and research to benefit children. Dr. John Hutton is the Vice President and Director of Biomedical Informatics at Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA. He is also Professor of Pediatrics and Associate Dean for Information Services at the University of Cincinnati College of Medicine. |
| computer intensive methods for testing hypotheses: Single-case and Small-n Experimental Designs John B. Todman, Pat Dugard, 2001-03 This book is a practical guide to help researchers draw valid causal inferences from small-scale clinical intervention studies. It should be of interest to teachers of, and students in, courses with an experimental clinical component, as well as clinical researchers. Inferential statistics used in the analysis of group data are frequently invalid for use with data from single-case experimental designs. Even non-parametric rank tests provide, at best, approximate solutions for only some single-case (and small-n ) designs. Randomization (Exact) tests, on the other hand, can provide valid statistical analyses for all designs that incorporate a random procedure for assigning treatments to subjects or observation periods, including single-case designs. These Randomization tests require large numbers of data rearrangements and have been seldom used, partly because desktop computers have only recently become powerful enough to complete the analyses in a reasonable time. Now that the necessary computational power is available, they continue to be under-used because they receive scant attention in standard statistical texts for behavioral researchers and because available programs for running the analyses are relatively inaccessible to researchers with limited statistical or computing interest. This book is first and foremost a practical guide, although it also presents the theoretical basis for Randomization tests. Its most important aim is to make these tests accessible to researchers for a wide range of designs. It does this by providing programs on CD-ROM that allow users to run analyses of their data within a standard package (Minitab, Excel, or SPSS) with which they are already familiar. No statistical or computing expertise is required to use these programs. This is the new stats for single-case and small-n intervention studies, and anyone interested in this research approach will benefit. |
| computer intensive methods for testing hypotheses: Advances in Machine Learning and Cybernetics Daniel S. Yeung, Zhi-Qiang Liu, Xi-Zhao Wang, Hong Yan, 2006-05-05 This book constitutes the thoroughly refereed post-proceedings of the 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, held in Guangzhou, China in August 2005. The 114 revised full papers of this volume are organized in topical sections on agents and distributed artificial intelligence, control, data mining and knowledge discovery, fuzzy information processing, learning and reasoning, machine learning applications, neural networks and statistical learning methods, pattern recognition, vision and image processing. |
| computer intensive methods for testing hypotheses: Analyzing Social Networks Stephen P Borgatti, Martin G Everett, Jeffrey C Johnson, Filip Agneessens, 2024-02-22 Kickstart your research with this practical, bestselling guide to doing social network analysis. Get to grips with the mathematical foundations and learn how to use software tools such as NeTDraw and UNICET to reach your research goals. Supporting you step-by-step through the entire research process, from design and data collection to coding, visualisation, and analysis, the book also offers: • Case studies and examples using real data • Exercises drawn from the authors’ decades of teaching experience • Online access to datasets, worked examples and a software manual to help you practice your skills. Whether you are new to social network analysis or an experienced researcher, this approachable book is your technical toolbox and research companion all in one. |
| computer intensive methods for testing hypotheses: Evidence-Based Technical Analysis David Aronson, 2011-07-11 Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals. Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining. |
Computer | Definition, History, Operating Systems, & Facts
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