A Practical Guide to Data Analysis for Physical Science Students

Author: Louis Lyons,Lyons Louis

Publisher: Cambridge University Press

ISBN: 9780521424639

Category: Science

Page: 95

View: 5085

This textbook is intended for undergraduates who are carrying out laboratory experiments in the physical sciences for the first time. It is a practical guide on how to analyze data and estimate errors. The necessary formulas for performing calculations are given, and the ideas behind them are explained, although this is not a formal text on statistics. Specific examples are worked through step by step in the text. Emphasis is placed on the need to think about whether a calculated error is sensible. Students should take this book with them to the laboratory, and the format is intended to make this convenient. The book will provide the necessary understanding of what is involved, should inspire confidence in the method of estimating errors, and enable numerical calculations without too much effort.

Statistical Data Analysis

Author: Glen Cowan

Publisher: Oxford University Press

ISBN: 0198501560

Category: Mathematics

Page: 197

View: 487

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).

Resampling Methods

A Practical Guide to Data Analysis

Author: Phillip I. Good

Publisher: Springer Science & Business Media

ISBN: 081764444X

Category: Mathematics

Page: 218

View: 2026

This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware. New to the third edition are additional program listings and screen shots of C++, CART, Blossom, Box Sampler (an Excel add-in), EViews, MATLAB, R, Resampling Stats, SAS macros, S-Plus, Stata, or StatXact, which accompany each resampling procedure. A glossary and solutions to selected exercises have also been added. With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods. It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and technology.

Practical Statistics for Astronomers

Author: J. V. Wall,C. R. Jenkins

Publisher: Cambridge University Press

ISBN: 1107393701

Category: Science

Page: N.A

View: 4631

Astronomy needs statistical methods to interpret data, but statistics is a many-faceted subject that is difficult for non-specialists to access. This handbook helps astronomers analyze the complex data and models of modern astronomy. This second edition has been revised to feature many more examples using Monte Carlo simulations, and now also includes Bayesian inference, Bayes factors and Markov chain Monte Carlo integration. Chapters cover basic probability, correlation analysis, hypothesis testing, Bayesian modelling, time series analysis, luminosity functions and clustering. Exercises at the end of each chapter guide readers through the techniques and tests necessary for most observational investigations. The data tables, solutions to problems, and other resources are available online at www.cambridge.org/9780521732499. Bringing together the most relevant statistical and probabilistic techniques for use in observational astronomy, this handbook is a practical manual for advanced undergraduate and graduate students and professional astronomers.

Practical Data Analysis in Chemistry

Author: Marcel Maeder,Yorck-Michael Neuhold

Publisher: Elsevier

ISBN: 9780080548838

Category: Science

Page: 340

View: 7824

The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses. * Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes. * Provides examples of routines that are easily adapted to the processes investigated by the reader * 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered

A Student's Guide to Data and Error Analysis

Author: Herman J. C. Berendsen

Publisher: Cambridge University Press

ISBN: 1139497855

Category: Technology & Engineering

Page: N.A

View: 1311

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.

Data Analysis Methods in Physical Oceanography

Author: William J. Emery,Richard E. Thomson

Publisher: Elsevier

ISBN: 9780444507563

Category: Science

Page: 638

View: 6208

This is a practical reference guide to established and modern data analysis techniques in earth and ocean sciences. This second and revised edition has an additional appendix on Convolution and Fourier transforms. Intended for both students and established scientists, the five major chapters of the book cover data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. Chapter 5, on time series analysis, spans a wide diversity of topics - from stochastic processes and stationarity, coherence functions, Fourier analysis, tidal harmonic analysis, spectral and cross-spectral analysis, wavelet and other related methods for processing nonstationary data series, digital filters, and fractals. The seven appendices include unit conversions, approximation methods and nondimensional numbers used in geophysical fluid dynamics, presentations on convolution, statistical terminology, and distribution functions, and a number of important statistical tables.

Einführung in Statistik und Messwertanalyse für Physiker


Author: G. Bohm,G. Zech

Publisher: N.A

ISBN: 9783540257592


Page: 400

View: 6251

Die Einf]hrung in die Statistik und Messwertanalyse f]r Physiker richtet sich weniger an mathematischen \berlegungen aus, sondern stellt die praktische Anwendung in den Vordergrund und schdrft die Intuition experimentelle Ergebnisse richtig einzuschdtzen. Zahlreiche ausf]hrlich betrachtete Beispiele dienen dazu, hdufig bei der Datenanalyse gemachte Fehler zu vermeiden (unsinnige Anwendung des Chi-Quadrattests, Funktionenanpassung bei falscher Parametrisierung, Entfaltung mit willk]rlicher Regularisierung). Ein besonderes Augenmerk wird auf den Vergleich von Daten mit Monte-Carlo-Simulationen gelenkt. Moderne Experimente kommen nicht ohne Simulation aus. Deshalb ist es wichtig zu wissen, wie Parameteranpassungen und Entfaltungen in diesem Fall durchgef]rt werden. Au_erdem werden den Studierenden moderne Entwicklungen der Statistik nahegebracht, die in dlteren Lehrb]chern nicht behandelt werden.

Data Analysis

A Bayesian Tutorial

Author: Devinderjit Sivia,John Skilling

Publisher: OUP Oxford

ISBN: 0198568320

Category: Mathematics

Page: 264

View: 9294

Focusing on Bayesian methods and maximum entropy, this book shows how a few fundamental rules can be used to tackle a variety of problems in data analysis. Topics covered include reliability analysis, multivariate optimisation, least-squares and maximum likelihood, and more.

Excel 2013 for Physical Sciences Statistics

A Guide to Solving Practical Problems

Author: Thomas J Quirk,Meghan H. Quirk,Howard F. Horton

Publisher: Springer

ISBN: 3319289640

Category: Mathematics

Page: 242

View: 4186

This book shows the is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical science problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2013 for Physical Sciences Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.

Statistics, Data Mining, and Machine Learning in Astronomy

A Practical Python Guide for the Analysis of Survey Data

Author: Željko Ivezić,Andrew J. Connolly,Jacob T VanderPlas,Alexander Gray

Publisher: Princeton University Press

ISBN: 1400848911

Category: Science

Page: 552

View: 1538

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

Compendium of Practical Astronomy

Author: G. D. ROTH (Ed)

Publisher: Springer Science & Business Media

ISBN: 9783540535966

Category: Astronomy

Page: 540

View: 3772

The Compendium of Practical Astronomy is a revised and enlarged English version of the fourth edition of G. Roth's famous handbook for stargazers. In three volumes 28 carefully edited articles, aimed especially at amateur astronomers and students and teachers of astronomy in high schools and colleges, cover the length and breadth of practical astronomy. Volume 1 contains information on modern instrumentation and reduction techniques, including spherical astronomy, error estimations, telescope mountings, astrophotography, and more. Volume 2 covers the planetary system, with contributions on artificial satellites, comets, the polar aurorae, and the effects of the atmosphere on observational data. Volume 3 is devoted to stellar objects, variable stars and binary stars in particular. An introduction to the astronomical literature and a comprehensive chapter on astronomy education and instructional aids make the Compendium a useful complement to any college library, in addition to its being essential reading for all practical astronomers.

Advanced Statistics for Kinesiology and Exercise Science

A Practical Guide to ANOVA and Regression Analyses

Author: Moh H. Malek,Jared W. Coburn,William D. Marelich

Publisher: Routledge

ISBN: 1351864564

Category: Sports & Recreation

Page: 138

View: 5924

Advanced Statistics for Kinesiology and Exercise Science is the first textbook to cover advanced statistical methods in the context of the study of human performance. Divided into three distinct sections, the book introduces and explores in depth both analysis of variance (ANOVA) and regressions analyses, including chapters on: preparing data for analysis; one-way, factorial, and repeated-measures ANOVA; analysis of covariance and multiple analyses of variance and covariance; diagnostic tests; regression models for quantitative and qualitative data; model selection and validation; logistic regression Drawing clear lines between the use of IBM SPSS Statistics software and interpreting and analyzing results, and illustrated with sport and exercise science-specific sample data and results sections throughout, the book offers an unparalleled level of detail in explaining advanced statistical techniques to kinesiology students. Advanced Statistics for Kinesiology and Exercise Science is an essential text for any student studying advanced statistics or research methods as part of an undergraduate or postgraduate degree programme in kinesiology, sport and exercise science, or health science.


A Practical Guide

Author: Steve Cannon

Publisher: John Wiley & Sons

ISBN: 1118746724

Category: Science

Page: 224

View: 9078

Petrophysics is the science of evaluating the rock and fluid properties of oil, gas and water reservoirs through the acquisition of physical samples, electrical, chemical, nuclear and magnetic data acquired by surface logging, downhole coring, and drilling and wireline sondes. The evaluation, analysis and interpretation of this data is as much an art as a science as it requires an understanding of geology, chemistry, physics, electronics, mechanics and drilling technology. The techniques have been developed over the last 100 years primarily by the oil and gas industry, but the principles are equally relevant in coal mining, hydrogeology and environmental science. This book is firmly aimed at students of geology and petroleum engineering looking for a practical understanding of the background and workflows required to complete a petrophysical study of a well, a reservoir or a field. Petrophysics is log analysis constrained by geology, and if we ignore the rocks we risk making poor investment decisions.

New Scientist

Author: N.A

Publisher: N.A


Category: Science

Page: N.A

View: 6575