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Author: Peter J. Diggle
Publisher: Oxford University Press
ISBN: 0199543186
Category : Mathematics
Languages : en
Pages : 192
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Book Description
An antidote to technique-orientated approaches, this text avoids the recipe-book style, giving the reader a clear understanding of how core statistical ideas of experimental design, modelling, and data analysis are integral to the scientific method. No prior knowledge of statistics is required and a range of scientific disciplines are covered.
Author: Peter J. Diggle
Publisher: Oxford University Press
ISBN: 0199543186
Category : Mathematics
Languages : en
Pages : 192
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Book Description
An antidote to technique-orientated approaches, this text avoids the recipe-book style, giving the reader a clear understanding of how core statistical ideas of experimental design, modelling, and data analysis are integral to the scientific method. No prior knowledge of statistics is required and a range of scientific disciplines are covered.
Author: Glen McPherson
Publisher: Springer Science & Business Media
ISBN: 1475742908
Category : Business & Economics
Languages : en
Pages : 689
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Book Description
In this book I have taken on the challenge of providing an insight into Statistics and a blueprint for statistical application for a wide audience. For students in the sciences and related professional areas and for researchers who may need to apply Statistics in the course of scientific experimenta tion, the development emphasizes the manner in which Statistics fits into the framework of the scientific method. Mathematics students will find a unified, but non-mathematical structure for Statistics which can provide the motivation for the theoretical development found in standard texts on theoretical Statistics. For statisticians and students of Statistics, the ideas contained in the book and their manner of development may aid in the de velopment of better communications between scientists and statisticians. The demands made of readers are twofold: a minimal mathematical prerequisite which is simply an ability to comprehend formulae containing mathematical variables, such as those derived from a high school course in algebra or the equivalent; a grasp of the process of scientific modeling which comes with ei ther experience in scientific experimentation or practice with solving mathematical problems.
Author: W.M. Mason
Publisher: Springer Science & Business Media
ISBN: 1461385369
Category : Business & Economics
Languages : en
Pages : 402
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Book Description
The existence of the present volume can be traced to methodological concerns about cohort analysis, all of which were evident throughout most of the social sciences by the late 1970s. For some social scientists, they became part of a broader discussion concerning the need for new analytical techniques for research based on longitudinal data. In 1976, the Social Science Research Council (SSRC), with funds from the National Institute of Education, established a Committee on the Methodology of Longitudinal Research. (The scholars who comprised this committee are listed at the front of this volume. ) As part of the efforts of this Committee, an interdisciplinary conference on cohort analysis was held in the summer of 1979, in Snowmass, Colorado. Much of the work presented here stems from that conference, the purpose of which was to promote the development of general methodological tools for the study of social change. The conference included five major presentations by (1) William Mason and Herbert Smith, (2) Karl J6reskog and Dag S6rbom, (3) Gregory Markus, (4) John Hobcraft, Jane Menken and Samuel Preston, and (5) Stephen Fienberg and William Mason. The formal presentations were each followed by extensive discussion, which involved as participants: Paul Baltes, William Butz, Philip Converse, Otis Dudley Duncan, David Freedman, William Meredith, John Nesselroade, Daniel Price, Thomas Pullum, Peter Read, Matilda White Riley, Norman Ryder, Warren Sanderson, Warner Schaie, Burton Singer, Nancy Tuma, Harrison White, and Halliman Winsborough.
Author: John Staddon
Publisher: Routledge
ISBN: 1351586890
Category : Psychology
Languages : en
Pages : 164
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Book Description
This book shows how science works, fails to work, or pretends to work, by looking at examples from such diverse fields as physics, biomedicine, psychology, and economics. Social science affects our lives every day through the predictions of experts and the rules and regulations they devise. Sciences like economics, sociology and health are subject to more ‘operating limitations’ than classical fields like physics or chemistry or biology. Yet, their methods and results must also be judged according to the same scientific standards. Every literate citizen should understand these standards and be able to tell the difference between good science and bad. Scientific Method enables readers to develop a critical, informed view of scientific practice by discussing concrete examples of how real scientists have approached the problems of their fields. It is ideal for students and professionals trying to make sense of the role of science in society, and of the meaning, value, and limitations of scientific methodology in the social sciences.
Author: Peter Westfall
Publisher: CRC Press
ISBN: 1466512105
Category : Mathematics
Languages : en
Pages : 572
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Book Description
Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method. With a focus on statistical models as producers of data, the book enables students to more easily understand the machinery of advanced statistics. It also downplays the "population" interpretation of statistical models and presents Bayesian methods before frequentist ones. Requiring no prior calculus experience, the text employs a "just-in-time" approach that introduces mathematical topics, including calculus, where needed. Formulas throughout the text are used to explain why calculus and probability are essential in statistical modeling. The authors also intuitively explain the theory and logic behind real data analysis, incorporating a range of application examples from the social, economic, biological, medical, physical, and engineering sciences. Enabling your students to answer the why behind statistical methods, this text teaches them how to successfully draw conclusions when the premises are flawed. It empowers them to use advanced statistical methods with confidence and develop their own statistical recipes. Ancillary materials are available on the book’s website.
Author: Hugh G. Gauch
Publisher: Cambridge University Press
ISBN: 9780521017084
Category : Science
Languages : en
Pages : 458
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Book Description
This textbook will enable scientists to be better scientists by offering them a deeper understanding of the scientific method.
Author: Janie H. Wilson
Publisher: SAGE Publications
ISBN: 1483392163
Category : Psychology
Languages : en
Pages : 441
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Book Description
This innovative text offers a completely integrated approach to teaching research methods and statistics by presenting a research question accompanied by the appropriate methods and statistical procedures needed to address it. Research questions and designs become more complex as chapters progress, building on simpler questions to reinforce student learning. Using a conversational style and research examples from published works, this comprehensive book walks readers through the entire research process and includes ample pedagogical support for SPSS, Excel, and APA style.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309486165
Category : Science
Languages : en
Pages : 257
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Book Description
One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.
Author: David Aronson
Publisher: John Wiley & Sons
ISBN: 1118160584
Category : Business & Economics
Languages : en
Pages : 572
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Book Description
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.
Author: Massimiliano Bonamente
Publisher: Springer
ISBN: 1493965727
Category : Science
Languages : en
Pages : 318
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Book Description
The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: • a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. • a new chapter on the various measures of the mean including logarithmic averages. • new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. • a new case study and additional worked examples. • mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text. • end-of-chapter summary boxes, for easy reference. As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.
