Statistical Data Analysis

Author: Glen Cowan

Publisher: Oxford University Press

ISBN: 0198501560

Category: Mathematics

Page: 197

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This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).

Data Analysis

A Bayesian Tutorial

Author: D. S. Sivia

Publisher: Oxford University Press

ISBN: 9780198518891

Category: Science

Page: 189

View: 3366

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Statistics lectures have often been viewed with trepidation by engineering and science students taking an ancillary course in this subject. Whereas there are many texts showing "how" statistical methods are applied, few provide a clear explanation for non-statisticians of how the principlesof data analysis can be based on probability theory. Data Analysis: A Bayesian Tutorial provides such a text, putting emphasis as much on understanding "why" and "when" certain statistical procedures should be used as "how". This difference in approach makes the text ideal as a tutorial guide forsenior undergraduates and research students, in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. With its central emphasis on a fewfundamental rules, this book takes the mystery out of statistics by providing a clear rationale for some of the most widely-used procedures.

Searches for Dijet Resonances

Using √s = 13 TeV Proton–Proton Collision Data Recorded by the ATLAS Detector at the Large Hadron Collider

Author: Lydia Audrey Beresford

Publisher: Springer

ISBN: 331997520X

Category: Science

Page: 169

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This book addresses one of the most intriguing mysteries of our universe: the nature of dark matter. The results presented here mark a significant and substantial contribution to the search for new physics, in particular for new particles that couple to dark matter. The first analysis presented is a search for heavy new particles that decay into pairs of hadronic jets (dijets). This pioneering analysis explores unprecedented dijet invariant masses, reaching nearly 7 TeV, and sets constraints on several important new physics models. The two subsequent analyses focus on the difficult low dijet mass region, down to 200 GeV, and employ a novel technique to efficiently gather low-mass dijet events. The results of these analyses transcend the long-standing constraints on dark matter mediator particles set by several existing experiments.

Error Analysis with Applications in Engineering

Author: Zbigniew A. Kotulski,Wojciech Szczepinski

Publisher: Springer Science & Business Media

ISBN: 9048135702

Category: Technology & Engineering

Page: 270

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Our intention in preparing this book was to present in as simple a manner as possible those branches of error analysis which ?nd direct applications in solving various problems in engineering practice. The main reason for writing this text was the lack of such an approach in existing books dealing with the error calculus. Most of books are devoted to mathematical statistics and to probability theory. The range of applications is usually limited to the problems of general statistics and to the analysis of errors in various measuring techniques. Much less attention is paid in these books to two-dimensional and three-dim- sional distributions, and almost no attention is given to problems connected with the two-dimensional and three-dimensional vectorial functions of independent random variables. The theory of such vectorial functions ?nds new applications connected, for example, with analysis of the positioning accuracy of various mechanisms, among them of robot manipulators and automatically controlled earth-moving and loading machines, such as excavators.

Practical Methods for Reliability Data Analysis

Author: J. I. Ansell,M. J. Phillips

Publisher: Oxford University Press

ISBN: 9780198536642

Category: Mathematics

Page: 240

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This is a practical text for those who wish to analyse data from Reliability studies. The emphasis is on clear explanation of the techniques used, supported by extensive mathematical and statistical background and nature of the data before it is analysed. There are chapters on survivalanalysis, using illuminating case studies.

Data Analysis

A Bayesian Tutorial

Author: Devinderjit Sivia,John Skilling

Publisher: Oxford University Press

ISBN: 0198568312

Category: Mathematics

Page: 246

View: 8842

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Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design. The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.

Datenanalyse mit Python

Auswertung von Daten mit Pandas, NumPy und IPython

Author: Wes McKinney

Publisher: O'Reilly

ISBN: 3960102143

Category: Computers

Page: 542

View: 7444

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Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar.Aus dem Inhalt:Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.

Chemometrics in Environmental Analysis

Author: Jürgen W. Einax,Heinz W. Zwanziger,Sabine Geiß

Publisher: John Wiley & Sons

ISBN: 9783527287727

Category: Science

Page: 384

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J. W. Einax, H. W. Zwanziger S. Gei Chemometrics in Environmental Analysis Make the most of your data! This new title will serve both as an introduction and as a practical guide to those techniques of chemometrics which are applicable to environmental analysis. By describing the optimum methods of data analysis it will help all chemists in this field to save time and money. Because the authors demonstrate the most important chemometric methods with the aid of numerous examples, the reader will learn to solve a given problem by use of the appropriate method. Applications range from sampling, through laboratory analysis, to evaluation. Interpretation of the findings is explained clearly. The text covers not only basic methods such as univariate statistics, regression analysis, and statistical test planning, but also multivariate data analysis, for example, cluster analysis, principal components analysis, and factor and discriminant analysis. Case studies show the enormous possibilities, and the limits, of chemometric methods. The book will help all environmental analytical scientists, even those with only a basic knowledge of mathematics, to optimize the evaluation and interpretation of the results of their measurements.

Aspects of Multivariate Statistical Analysis in Geology

Author: E. Savazzi,R.A. Reyment

Publisher: Elsevier

ISBN: 9780080527611

Category: Science

Page: 285

View: 6997

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The book presents multivariate statistical methods useful in geological analysis. The essential distinction between multivariate analysis as applied to full-space data (measurements on lengths, heights, breadths etc.) and compositional data is emphasized with particular reference to geochemical data. Each of the methods is accompanied by a practically oriented computer program and backed up by appropriate examples. The computer programs are provided on a compact disk together with trial data-sets and examples of the output. An important feature of this book is the graphical system developed by Dr. Savazzi which is entitled Graph Server. Geological data often deviate from ideal statistical requirements. For this reason, close attention has been paid to the analysis of data that contain atypical observations.

The Oxford Handbook of Panel Data

Author: Badi H. Baltagi

Publisher: Oxford University Press

ISBN: 0190210826

Category: Social Science

Page: 512

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The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.

Statistik-Workshop für Programmierer

Author: Allen B. Downey

Publisher: O'Reilly Germany

ISBN: 3868993436

Category: Computers

Page: 160

View: 8387

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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Author: Raphaël Mourad

Publisher: OUP Oxford

ISBN: 0191019208

Category: Mathematics

Page: 464

View: 9790

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Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.

Statistical Models in Epidemiology

Author: David Clayton,Michael Hills

Publisher: OUP Oxford

ISBN: 0191650919

Category: Medical

Page: 384

View: 1216

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This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.

Your Statistical Consultant

Answers to Your Data Analysis Questions

Author: Rae R. Newton,Kjell Erik Rudestam

Publisher: SAGE Publications

ISBN: 1452289654

Category: Social Science

Page: 384

View: 8024

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Although many graduate students and researchers have had course work in statistics, they sometimes find themselves stumped in proceeding with a particular data analysis question. In fact, statistics is often taught as a lesson in mathematics as opposed to a strategy for answering questions about world[?], leaving beginning researchers at a loss for how to proceed. In these situations, it is common to turn to a statistical expert, the "go to" person when questions regarding appropriate data analysis emerge. Your Statistical Consultant is an authentic alternative resource for describing, explaining, and making recommendations regarding thorny or confusing statistical issues. Written to be responsive to a wide range of inquiries and levels of expertise, this book is flexibly organized so readers can either read it sequentially or turn directly to the sections that correspond to their concerns and questions.

Statistical Modelling in GLIM

Author: Murray Aitkin

Publisher: Oxford University Press

ISBN: 9780198522034

Category: Mathematics

Page: 374

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The analysis of data by statistical modelling is becoming increasingly important. This book presents both the theory of statistical modelling with generalized linear models and the application of the theory to practical problems using the widely available package GLIM. The authors have takenpains to integrate the theory with many practical examples which illustrate the value of interactive statistical modelling. Throughout the book theoretical issues of formulating and simplifying models are discussed, as are problems of validating the models by the detection of outliers and influential observations. The book arises from short courses given at the University of Lancaster's Centre for Applied Statistics, with an emphasis on practical programming in GLIM and numerous examples. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential andWeibull distributions. A feature of the book is a detailed discussion of survival analysis. Statisticians working in a wide range of fields, including biomedical and social sciences, will find this book an invaluable desktop companion to aid their statistical modelling. It will also provide a text for students meeting the ideas of statistical modelling for the first time.

Applied Smoothing Techniques for Data Analysis

The Kernel Approach with S-Plus Illustrations

Author: A. W. Bowman,Adelchi Azzalini,Azzalini Bowman

Publisher: Oxford University Press

ISBN: 0198523963

Category: Mathematics

Page: 193

View: 9439

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The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can beapplied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawnfrom a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level,as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scriptsare provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.

Bayesian Statistics 9

Author: José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman

Publisher: Oxford University Press

ISBN: 0199694583

Category: Mathematics

Page: 706

View: 2486

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Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.