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Basic Concepts Of Data And Error Analysis
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Book Synopsis Basic Concepts of Data and Error Analysis by : Panayiotis Nicos Kaloyerou
Download or read book Basic Concepts of Data and Error Analysis written by Panayiotis Nicos Kaloyerou and published by . This book was released on 2018 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introductory textbook explains the concepts and methods of data and error analysis needed for laboratory experiment write-ups, especially physics and engineering experiments. The book contains the material needed for beginning students, e.g., first year university students, college students (enrolled on a certificate or diploma course) and even A-level students. Nevertheless, it also covers the required material for higher year university laboratories, including the final year. Only essential concepts and methods needed for the day-to-day performance of experiments and their subsequent analysis and presentation are included and, at the same time, presented as simply as possible. Non-essential detail is avoided. Chapter five is a stand-alone introduction to probability and statistics aimed at providing a theoretical background to the data and error analysis chapters one to four. Computer methods are introduced in Chapter six. The author hopes this book will serve as a constant reference.
Book Synopsis Basic Concepts of Data and Error Analysis by : Panayiotis Nicos Kaloyerou
Download or read book Basic Concepts of Data and Error Analysis written by Panayiotis Nicos Kaloyerou and published by Springer. This book was released on 2018-10-24 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introductory textbook explains the concepts and methods of data and error analysis needed for laboratory experiment write-ups, especially physics and engineering experiments. The book contains the material needed for beginning students, e.g., first year university students, college students (enrolled on a certificate or diploma course) and even A-level students. Nevertheless, it also covers the required material for higher year university laboratories, including the final year. Only essential concepts and methods needed for the day-to-day performance of experiments and their subsequent analysis and presentation are included and, at the same time, presented as simply as possible. Non-essential detail is avoided. Chapter five is a stand-alone introduction to probability and statistics aimed at providing a theoretical background to the data and error analysis chapters one to four. Computer methods are introduced in Chapter six. The author hopes this book will serve as a constant reference.
Book Synopsis Introduction to Error Analysis by : Jack Merrin
Download or read book Introduction to Error Analysis written by Jack Merrin and published by Createspace Independent Publishing Platform. This book was released on 2017-08-29 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Great scientists master the math behind the science. Do you still delay mastering data analysis, keeping you from more accurate, rigorous, and higher certainty conclusions? Jack Merrin, Ph.D. Princeton University, is a physicist who has helped hundreds of students with math and physics, taught physics labs, and used error analysis through 25 years of research. You can surely learn the right statistical methods from Jack. Introduction to Error Analysis is more than a collection of ad-hoc statistical theory. It is an easy-to-read blueprint used by scientists for presenting correct results. Transform your experimental perspective to confidence. Learn reusable principles for each new scientific project. This book covers reporting measurements and uncertainties, propagation of error, combining results, curve fitting, essential statistical concepts, and much, much, more. You might love this book if: You are doing lab reports or actual research, and it's time to get serious about data analysis. You want to focus on the essential calculations, not on time-wasting theory. You want adaptable MATLAB code for each different calculation. Hey, no need to reinvent the wheel. You want to reach correct and unique results using the established convention. You want to know what is correct to spot bad scientific literature. Introduction to Error Analysis is the concise book you need to start building your successful scientific career. If you like easy-to-follow lessons, practical examples, insightful tips, and an author who actually cares about you getting it right, then you'll love Jack's book. Buy Introduction to Error Analysis to start refining your data analysis skills today!
Book Synopsis Data Reduction and Error Analysis for the Physical Sciences by : Philip R. Bevington
Download or read book Data Reduction and Error Analysis for the Physical Sciences written by Philip R. Bevington and published by McGraw-Hill Science, Engineering & Mathematics. This book was released on 1992 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed as a laboratory companion, student textbook or reference book for professional scientists. The text is for use in one-term numerical analysis, data and error analysis, or computer methods courses, or for laboratory use. It is for the sophomore-junior level, and calculus is a prerequisite. The new edition includes applications for PC use.
Book Synopsis An Introduction to Error Analysis by : John Robert Taylor
Download or read book An Introduction to Error Analysis written by John Robert Taylor and published by Univ Science Books. This book was released on 1997-01-01 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Problems after each chapter
Book Synopsis A Student's Guide to Data and Error Analysis by : Herman J. C. Berendsen
Download or read book A Student's Guide to Data and Error Analysis written by Herman J. C. Berendsen and published by Cambridge University Press. This book was released on 2011-04-07 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: All students taking laboratory courses within the physical sciences and engineering will benefit from this book, whilst researchers will find it an invaluable reference. This concise, practical guide brings the reader up-to-speed on the proper handling and presentation of scientific data and its inaccuracies. It covers all the vital topics with practical guidelines, computer programs (in Python), and recipes for handling experimental errors and reporting experimental data. In addition to the essentials, it also provides further background material for advanced readers who want to understand how the methods work. Plenty of examples, exercises and solutions are provided to aid and test understanding, whilst useful data, tables and formulas are compiled in a handy section for easy reference.
Book Synopsis Statistics in a Nutshell by : Sarah Boslaugh
Download or read book Statistics in a Nutshell written by Sarah Boslaugh and published by "O'Reilly Media, Inc.". This book was released on 2012-11-15 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: A clear and concise introduction and reference for anyone new to the subject of statistics.
Book Synopsis An Introduction to the Concept of Error Analysis by : Robert Wetzorke
Download or read book An Introduction to the Concept of Error Analysis written by Robert Wetzorke and published by GRIN Verlag. This book was released on 2010-01-08 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seminar paper from the year 2005 in the subject Didactics - English - Pedagogy, Literature Studies, grade: 1,3, Technical University of Braunschweig (Englisches Seminar), language: English, abstract: Foreign Language Pedagogy (FLP), in general, aims to convey to teachers the essential information about the role of the learner and the teacher in the process of language learning, and also provides them with theoretical, didactic methods and practical means for the foreign language classroom (FLC). We can even go a step further by claiming that the mission of FLP is to research for and establish the supreme way of a teaching a foreign language (FL) to the learners. However, within this field of research it becomes quite obvious that the learners take in a rather passive role and do not contribute very much to new research data and, hence, new approaches towards foreign language teaching (FLT). This thesis can be held true, to give just one example, when we consider the various teaching methods for the FLC. Although the role of the learner is taken into account in each method, the learners are fairly more than “testing objects” of teaching models hypothesized by didactic scientists. On the other hand, one must admit that in correspondence with the recent emergence and establishment of the communicative approach (CA), the learners preferences and demands have been taken far more into consideration and their linguistic and communicative performance serve as source for methodological research input and constructive, teacher strategies-oriented as well as learner strategies-oriented output offered by science. Recently, and paradoxically enough, it can be perceived intensive discussion concerning the question how to deal best with errors produced by learners. More precisely, there has been a shift from the formerly applied “Contrastive Analysis” (CAH) toward the occupation with “Error Analysis” (EA). (...)
Book Synopsis Data Reduction and Error Analysis for the Physical Sciences by : Philip R. Bevington
Download or read book Data Reduction and Error Analysis for the Physical Sciences written by Philip R. Bevington and published by McGraw-Hill Companies. This book was released on 1969 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to provide an introduction to the concepts of statistical analysis of data for students at the undergraduate and graduate level, and to provide tools for data reduction and error analysis commonly required in the physical sciences. The presentation is developed from a practical point of view, including enough derivation to justify the results, but emphasizing methods of handling data more than theory. The text provides a variety of numerical and graphical techniques. Computer programs that support these techniques will be available on an accompanying website in both Fortran and C++.
Book Synopsis Data Analysis Methods in Physical Oceanography by : Richard E. Thomson
Download or read book Data Analysis Methods in Physical Oceanography written by Richard E. Thomson and published by Elsevier. This book was released on 2001-04-03 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis Methods in Physical Oceanography is a practical referenceguide to established and modern data analysis techniques in earth and oceansciences. This second and revised edition is even more comprehensive with numerous updates, and an additional appendix on 'Convolution and Fourier transforms'. Intended for both students and established scientists, the fivemajor chapters of the book cover data acquisition and recording, dataprocessing and presentation, statistical methods and error handling,analysis of spatial data fields, and time series analysis methods. Chapter 5on time series analysis is a book in itself, spanning a wide diversity oftopics from stochastic processes and stationarity, coherence functions,Fourier analysis, tidal harmonic analysis, spectral and cross-spectralanalysis, wavelet and other related methods for processing nonstationarydata series, digital filters, and fractals. The seven appendices includeunit conversions, approximation methods and nondimensional numbers used ingeophysical fluid dynamics, presentations on convolution, statisticalterminology, and distribution functions, and a number of importantstatistical tables. Twenty pages are devoted to references. Featuring:• An in-depth presentation of modern techniques for the analysis of temporal and spatial data sets collected in oceanography, geophysics, and other disciplines in earth and ocean sciences.• A detailed overview of oceanographic instrumentation and sensors - old and new - used to collect oceanographic data.• 7 appendices especially applicable to earth and ocean sciences ranging from conversion of units, through statistical tables, to terminology and non-dimensional parameters. In praise of the first edition: "(...)This is a very practical guide to the various statistical analysis methods used for obtaining information from geophysical data, with particular reference to oceanography(...)The book provides both a text for advanced students of the geophysical sciences and a useful reference volume for researchers." Aslib Book Guide Vol 63, No. 9, 1998 "(...)This is an excellent book that I recommend highly and will definitely use for my own research and teaching." EOS Transactions, D.A. Jay, 1999 "(...)In summary, this book is the most comprehensive and practical source of information on data analysis methods available to the physical oceanographer. The reader gets the benefit of extremely broad coverage and an excellent set of examples drawn from geographical observations." Oceanography, Vol. 12, No. 3, A. Plueddemann, 1999 "(...)Data Analysis Methods in Physical Oceanography is highly recommended for a wide range of readers, from the relative novice to the experienced researcher. It would be appropriate for academic and special libraries." E-Streams, Vol. 2, No. 8, P. Mofjelf, August 1999
Book Synopsis Software Error Analysis by : Wendy W. Peng
Download or read book Software Error Analysis written by Wendy W. Peng and published by Silicon Press. This book was released on 1994-10 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Introduction to Statistics by : Howard M. Reid
Download or read book Introduction to Statistics written by Howard M. Reid and published by SAGE Publications. This book was released on 2013-08-13 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using a truly accessible and reader-friendly approach, Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis, by Howard M. Reid, redefines the way statistics can be taught and learned. Unlike other books that merely focus on procedures, Reid’s approach balances development of critical thinking skills with application of those skills to contemporary statistical analysis. He goes beyond simply presenting techniques by focusing on the key concepts readers need to master in order to ensure their long-term success. Indeed, this exciting new book offers the perfect foundation upon which readers can build as their studies and careers progress to more advanced forms of statistics. Keeping computational challenges to a minimum, Reid shows readers not only how to conduct a variety of commonly used statistical procedures, but also when each procedure should be utilized and how they are related. Following a review of descriptive statistics, he begins his discussion of inferential statistics with a two-chapter examination of the Chi Square test to introduce students to hypothesis testing, the importance of determining effect size, and the need for post hoc tests. When more complex procedures related to interval/ratio data are covered, students already have a solid understanding of the foundational concepts involved. Exploring challenging topics in an engaging and easy-to-follow manner, Reid builds concepts logically and supports learning through robust pedagogical tools, the use of SPSS, numerous examples, historical quotations, insightful questions, and helpful progress checks.
Book Synopsis Data Reduction and Error Analysis for the Physical Sciences by : Philip Bevington
Download or read book Data Reduction and Error Analysis for the Physical Sciences written by Philip Bevington and published by . This book was released on 2003 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to provide an introduction to the concepts of statistical analysis of data for students at the undergraduate and graduate level, and to provide tools for data reduction and error analysis commonly required in the physical sciences. The presentation is developed from a practical point of view, including enough derivation to justify the results, but emphasizing methods of handling data more than theory. The text provides a variety of numerical and graphical techniques. Computer programs that support these techniques will be available on an accompanying website in both Fortran and C++.
Book Synopsis Core Concepts in Data Analysis: Summarization, Correlation and Visualization by : Boris Mirkin
Download or read book Core Concepts in Data Analysis: Summarization, Correlation and Visualization written by Boris Mirkin and published by Springer Science & Business Media. This book was released on 2011-04-05 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Core Concepts in Data Analysis: Summarization, Correlation and Visualization provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule). Boris Mirkin takes an unconventional approach and introduces the concept of multivariate data summarization as a counterpart to conventional machine learning prediction schemes, utilizing techniques from statistics, data analysis, data mining, machine learning, computational intelligence, and information retrieval. Innovations following from his in-depth analysis of the models underlying summarization techniques are introduced, and applied to challenging issues such as the number of clusters, mixed scale data standardization, interpretation of the solutions, as well as relations between seemingly unrelated concepts: goodness-of-fit functions for classification trees and data standardization, spectral clustering and additive clustering, correlation and visualization of contingency data. The mathematical detail is encapsulated in the so-called “formulation” parts, whereas most material is delivered through “presentation” parts that explain the methods by applying them to small real-world data sets; concise “computation” parts inform of the algorithmic and coding issues. Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.
Book Synopsis Comet for Data Science by : Angelica Lo Duca
Download or read book Comet for Data Science written by Angelica Lo Duca and published by Packt Publishing Ltd. This book was released on 2022-08-26 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain the key knowledge and skills required to manage data science projects using Comet Key Features • Discover techniques to build, monitor, and optimize your data science projects • Move from prototyping to production using Comet and DevOps tools • Get to grips with the Comet experimentation platform Book Description This book provides concepts and practical use cases which can be used to quickly build, monitor, and optimize data science projects. Using Comet, you will learn how to manage almost every step of the data science process from data collection through to creating, deploying, and monitoring a machine learning model. The book starts by explaining the features of Comet, along with exploratory data analysis and model evaluation in Comet. You'll see how Comet gives you the freedom to choose from a selection of programming languages, depending on which is best suited to your needs. Next, you will focus on workspaces, projects, experiments, and models. You will also learn how to build a narrative from your data, using the features provided by Comet. Later, you will review the basic concepts behind DevOps and how to extend the GitLab DevOps platform with Comet, further enhancing your ability to deploy your data science projects. Finally, you will cover various use cases of Comet in machine learning, NLP, deep learning, and time series analysis, gaining hands-on experience with some of the most interesting and valuable data science techniques available. By the end of this book, you will be able to confidently build data science pipelines according to bespoke specifications and manage them through Comet. What you will learn • Prepare for your project with the right data • Understand the purposes of different machine learning algorithms • Get up and running with Comet to manage and monitor your pipelines • Understand how Comet works and how to get the most out of it • See how you can use Comet for machine learning • Discover how to integrate Comet with GitLab • Work with Comet for NLP, deep learning, and time series analysis Who this book is for This book is for anyone who has programming experience, and wants to learn how to manage and optimize a complete data science lifecycle using Comet and other DevOps platforms. Although an understanding of basic data science concepts and programming concepts is needed, no prior knowledge of Comet and DevOps is required.
Book Synopsis Introduction to Data Science by : Rafael A. Irizarry
Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
