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Titlebook: Statistics and Analysis of Scientific Data; Massimiliano Bonamente Textbook 20172nd edition Springer Science+Business Media, LLC, part of

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发表于 2025-3-21 17:19:50 | 显示全部楼层 |阅读模式
书目名称Statistics and Analysis of Scientific Data
编辑Massimiliano Bonamente
视频video
概述Introduces the statistical techniques most commonly employed in physical sciences and engineering.Makes clear distinction between material that is strictly mathematical and theoretical, and practical
丛书名称Graduate Texts in Physics
图书封面Titlebook: Statistics and Analysis of Scientific Data;  Massimiliano Bonamente Textbook 20172nd edition Springer Science+Business Media, LLC, part of
描述.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 p
出版日期Textbook 20172nd edition
关键词Fitting Data with Bivariate Errors; Goodness of Fit and Parameter Uncertainty; Maximum Likelihood Fit;
版次2
doihttps://doi.org/10.1007/978-1-4939-6572-4
isbn_softcover978-1-4939-8239-4
isbn_ebook978-1-4939-6572-4Series ISSN 1868-4513 Series E-ISSN 1868-4521
issn_series 1868-4513
copyrightSpringer Science+Business Media, LLC, part of Springer Nature 2017
The information of publication is updating

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发表于 2025-3-21 23:31:54 | 显示全部楼层
Three Fundamental Distributions: Binomial, Gaussian, and Poisson,er of variables, and it is referred to as the . distribution. The Poisson distribution applies to counting experiments, and it can be obtained as the limit of the binomial distribution when the probability of success is small.
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Maximum Likelihood and Other Methods to Estimate Variables,n estimator of the parent mean. One of these methods, the maximum likelihood method, will later be used in more complex applications that involve the fit of two-dimensional data and the estimation of fit parameters. The concepts introduced in this chapter constitute the core of the statistical techniques for the analysis of scientific data.
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Model Comparison,f the . statistic, useful to compare the goodness of fit between two models and the need for an additional “nested” model component, and the Kolmogorov–Smirnov statistics, useful in providing a quantitative measure of the goodness of fit, and in comparing two datasets regardless of their fit to a specific model.
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Monte Carlo Markov Chains, values and confidence intervals. The modern-day data analyst will find that MCMCs are an essential tool that permits tasks that are simply not possible with other methods, such as the simultaneous estimate of parameters for multi-parametric models of virtually any level of complexity, even in the presence of correlation among the parameters.
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Theory of Probability,hod to assign the probability of an event, for example, the probability that a coin lands heads up after a toss. The .—or empirical—approach and the .—or Bayesian— approach are two methods that can be used to calculate probabilities. The fact that there is more than one method available for this pur
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Random Variables and Their Distributions,value of these quantities cannot be known with absolute precision, but rather we can constrain the variable to a given range of values, narrower or wider according to the nature of the variable itself and the type of experiment performed. Random variables are described by a distribution function, wh
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