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Advanced Excel for Scientific Data Analysis
     

Advanced Excel for Scientific Data Analysis

5.0 1
by Robert De Levie
 

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Combining an easy-going style with an emphasis on practical applications, this greatly expanded second edition is remarkable in scope and coverage. As reviews of the first edition noted, the term "advanced" in the title is not used lightly. Less than a third of its 700+ pages are devoted to least squares analysis, yet the reader will learn about many

Overview

Combining an easy-going style with an emphasis on practical applications, this greatly expanded second edition is remarkable in scope and coverage. As reviews of the first edition noted, the term "advanced" in the title is not used lightly. Less than a third of its 700+ pages are devoted to least squares analysis, yet the reader will learn about many aspects of this ubiquitous method that are seldom found together in one volume: multivariate and polynomial centering, the statistical uncertainty in uncertainty estimates, how to use the covariance, singular value decomposition, the pros and cons of weighted least squares, moving equidistant least squares, nonlinear least squares, and imprecision contours.

There are lucid chapters on Fourier transformation, convolution and deconvolution, and digital simulation of ordinary differential equations. A new chapter is devoted to some common but often only crudely used mathematical methods, such as numerical differentiation, Romberg integration, and cubic spline interpolation. Another new chapter shows how to use linear algebra on the spreadsheet with Volpi's extensive matrix toolbox of custom functions and macros. A third, newly added chapter describes how to set up the spreadsheet to make it less error-prone, and how to get superaccurate answers in Excel. The substantially enlarged chapter on writing functions and macros now has a set of MacroMorsels to illustrate specific points that otherwise might trip up novice programmers, and a detailed description of Excel's extensive debugging tools. All this is presented in an easily digestible format, illustrated with many examples from the literature, and supported by a large collection of open-access (i.e., fully transparent and user-modifiable) custom functions and macros.

Editorial Reviews

From the Publisher
"A very useful book that will help users familiar with Excel exploit its computational capacities to the fullest."—Journal of the American Chemical Society

Product Details

ISBN-13:
9780195370225
Publisher:
Oxford University Press, USA
Publication date:
08/14/2008
Edition description:
2nd Edition
Pages:
736
Product dimensions:
5.90(w) x 9.20(h) x 1.60(d)

Meet the Author

Robert de Levie earned his Ph.D. at the University of Amsterdam. He was a postdoctoral fellow with Paul Delahay at LSU in Baton Rouge LA for two years, and then for 34 years taught analytical chemistry and electrochemistry at Georgetown University in Washington DC. For ten of those years he was the US editor of the Journal of Electroanalytical Chemistry. Now an emeritus professor, he is associated with Bowdoin College.

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Advanced Excel for Scientific Data Analysis 5 out of 5 based on 0 ratings. 1 reviews.
Guest More than 1 year ago
Do you need a spreadsheet tool to analyze experimental data? If you do, then this book is for you! Author Robert De Levie, has written the second edition of an outstanding book on advanced Excel, that shows you how to conduct the numerical analysis of experimental data, such as are usually encountered in the physical sciences. De Levie, begins by describing some of the standard mathematical methods, such as numerical integration and differentiation, and how to perform these most accurately on the spreadsheet. Then, the author examines precision¿with random fluctuations and their reduction or removal. Next, he shows you how to apply the least squares methods to polynomials in the independent variable x, and to multivariable functions. The author continues by describing the nonlinear least squares method, where one compares a given data set with a model expression that depends on one or more numerical parameters. In addition, he also deals with the application of Fourier transformation in numerical data analysis, rather than instrumentation, where it is often built in. Then, the author discusses the use of time-dependent signals. He also describes particular types of errors: The algorithmic deviations caused by replacing a differential equation by an approximation thereof. Next, the author will show you how to copy spreadsheet data into a macro, manipulate them, and return the result to the spreadsheet. He continues by looking at some common mathematical operations, often encountered in scientific data analysis, and their numerical implementations on the spreadsheet. In addition, the author shows you how to extend the set of tools available for matrix operations in Excel. Finally, he focuses on three types of spreadsheet-related errors: those that are rather easy to make on a spreadsheet, those that result from Excel¿s adherence to the IEEE-754 protocol, and those that are in hidden in Excel. The author of this most excellent book has made a great effort to make it as broadly useful as possible to the reader, and to incorporate examples from different areas. More importantly, the author believes that this book offers instead, an attempt at the synthesis of different areas, thus illustrating how many numerical problems can be fitted comfortably in the convenient, user-friendly format of the spreasheet.