Advanced Excel for Scientific Data Analysis (Paperback)

by Robert De Levie (Author)

Editorial Reviews

Review

"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 Description
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.

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Product Details

• Paperback: 736 pages
• Publisher: Oxford University Press, USA; 2nd edition (August 14, 2008)
• Language: English
• ISBN-10: 0195370228
• ISBN-13: 978-0195370225
• Product Dimensions: 9.3 x 6.1 x 1.6 inches
• Shipping Weight: 2 pounds (View shipping rates and policies)
• Average Customer Review: 3.9 out of 5 stars See all reviews (14 customer reviews)
• Amazon.com Sales Rank: #64,266 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

40 of 40 people found the following review helpful:

5.0 out of 5 stars Excellent for scientists and engineers, March 25, 2004

By	johare "johare4" (Tucson, AZ United States) - See all my reviews

This review is from: Advanced Excel for Scientific Data Analysis (Paperback)

Advanced Excel does very well what it does, so your main concern is whether what it does interests you. The book is intended for engineers and scientists who do real computation, not intended for those making turnkey applications for businesses.

Three chapters describe the use of Excel for least squares fitting. Treatment is authoritative, including things like phantom relations, orthogonal polynomials, fitting to a Lorentzian, finding the derivative of data, and so forth. Although there is a lot of detail, it is well presented, and you will be able to follow without being an expert yourself. Less extensive but still detailed are chapters on Fourier analysis and on convolution and deconvolution. A brief introduction to numerical integration of ordinary differential equations is exactly that, introductory. Tons of references to other literature are provided.

So, if you have a specialized interest in these topics, this book is a must. What else is here?

Approximately the last half of the book is devoted to writing macros, and to a presentation of macros used in the first half of the book. The publisher maintains a web site where these can be downloaded, saving you the tedium and error of typing them into your computer from the book. The approach is to use message boxes to communicate with computation in VBA. VBA is used primarily as a programming language, and there is rather little about the Excel object model. You will learn very little about worksheet manipulation using VBA.

The reader with less interest in the applications, but an interest in applying Excel to their own problems, will also find a lot of interesting details here. The author knows a lot about Excel, and you will pick up not only the big picture, but also many useful details. For example, how to call Solver from a macro. How to line your charts up with the spreadsheet grid. How to make the most of Excel's graphic abilities.

This book is NOT the typical Excel book full of screen shots and low on content. It teaches by example. By going through the examples presented, you really will learn how to use Excel for your application too.

25 of 25 people found the following review helpful:

5.0 out of 5 stars Advanced Is Not Used Lightly in this Book's Title, July 27, 2005

By	John Matlock "Gunny" (Winnemucca, NV) - See all my reviews (REAL NAME)

This review is from: Advanced Excel for Scientific Data Analysis (Paperback)

If I had written this book I think I would have called it Scientific Excel rather than Advanced Excel. To be sure, the book is certainly for advanced Excel users, but it won't help you do an advanced business application.

You'd best have some knowledge about Excel before starting this one. There's a brief survey of Excel at the beginning that starts off comparing a spreadsheet to an accountant's ledger. That's pretty basic. Anyone with any Excel experience at all can follow the first three pages. On page four he is talking about making a thousand point plot with random numbers, normal distribution -- no longer something from Excel for Dummies. By page 5 he's calculating averages and standard deviations. By the end of this Survey chapter he's talking about the accuracy of the calculations performed by Excel.

Subsequent chapters discuss various types of mathematical manipulation that are often needed in the analysis of scientific data.

There are three chapters on Least Squares. This is the fitting of a curve to collected data so that the trends might be more easily visualized.

There is a chapter on Fourier Transformations, which is the probably the most frequently used analysis tool when working in signal processing. Geophysical seismic data, radar receivers, cell phone systems are all processed primarily using Fourier Transforms. This kind of data is of course too voluminous for Excel, but the techniques used here would be ideal for quite a number of laboratory applications.

A couple of chapters cover convolution, deconvolution, and time-frequency analysis as well as Numerical integration of ordinary differential equations.

All of these processing tasks are done using macros. These are described in the book, or can be downloaded from the author's website -- www.bowdoin.edu/~rdelevie/excellaneous/. This web site also includes some additional macros that enhance Excel's computationability when handling numbers of higher precision.

The final four chapters of the book are on writing your own or modifying existing macros, with an orientation to scientific analysis.

I consider this to be almost a mandatory book for anyone interested in using Excel to analysis scientific data.

21 of 22 people found the following review helpful:

4.0 out of 5 stars Scientific number-crunching with Excel, January 6, 2005

By	J. Tellinghuisen (Nashville, TN) - See all my reviews (REAL NAME)

This review is from: Advanced Excel for Scientific Data Analysis (Paperback)

This book by Robert de Levie is a thorough and comprehensive how-to guide to the use of the Excel program on common numerical tasks in physical science. It starts with a chapter that surveys the capabilities of the Excel program itself. It then continues with three chapters of progressively increasing sophistication on the method of least squares, followed by single chapters on Fourier transformation, convolution and deconvolution, and numerical solution of differential equations. The final four chapters are given over to the writing of macros and the author's presentation of the many macros he has developed in the course of solving the problems illustrated in the book. Readers should be aware that all of these macros, as well as the numerical data used in many of the examples, are also available in computer-readable form from the publisher's web site and, in fact, are available to purchasers and nonpurchasers alike.

I should acknowledge at the outset that I am very much NOT a fan of Excel. However, the program is by now so firmly established that there is little doubt of the value of the contents of this book to many in the intended audience of scientists and engineers. Moreover, there is also plenty of value for those of us who prefer to use computational tools other than Excel. Since my own primary interests relative to this book fall within the chapters on least-squares methods, that is where I will direct my specific comments.

As already noted, the book is about computations, not about theory, so although key working equations are often presented, they are seldom derived. Thus a beginner wanting to understand the method of least squares might want to consult another source to complement the "nuts and bolts" provided by the examples illustrated here.

Chapter 2 is devoted to the simplest of least-squares (LS) problems, unweighted fitting to a straight line (including one forced to go through the origin). This chapter also introduces the important topic of propagation of error (called propagation of imprecision by the author in an attempt to improve the terminology). A number of common applications are considered, the most important of which is probably the role of linear LS in calibration in analytical chemistry. This is, incidentally, an application where the common textbook expressions for error propagation lead to incorrect estimates of the imprecision; but de Levie "does it right."

Chapter 3 continues with linear LS, but now involving fitting to functions more complex than a straight line and often involving three or more adjustable parameters. (Note that the "linear" in linear LS refers to the manner in which the adjustable parameters occur in the fit function, not to the shape of the function itself; some authors refer to this as "multilinear.") The coverage begins with fitting to polynomials and is later extended to orthogonal polynomials. Toward the end of the chapter, weighted LS is introduced; this is needed to deal with the problem of transforming nonlinear fit relationships into linear ones, like exponentials (log transformation) and hyperbolic relationships (reciprocal transformation). Most of the examples in this chapter are from analytical and physical chemistry and are often encountered in the chemical teaching literature. These include the analysis of diatomic spectroscopic data (I2 and HCl), the analytical problem of estimating species abundances from UV-visible spectra of mixtures, and the treatment of enzyme kinetics data.

Chapter 4 turns to nonlinear LS, in which iterative methods are needed to obtain the solutions to the minimization problem at the heart of LS. The tool for accomplishing this task in Excel is the Solver routine. Solver has one glaring limitation, namely the failure to provide the statistical errors in the adjustable parameters. De Levie has solved that problem with his own macro, SolverAid. The capabilities of these routines are illustrated on a number of examples, again mostly from the realm of analytical chemistry and spectroscopy. Among the more unusual examples are fits of titration data, of discontinuous functions, and of continuous functions taken piecewise. Toward the end are included some illustrations of the performance of Solver on some benchmark nonlinear fitting problems provided by NIST (National Institute for Science and Technology).

I have personally checked many of the examples illustrated in these three chapters using other methods, and I can vouch for their general validity. In a few cases there are errors, but many of these have been corrected by the author since the first printing of the book. Users should consult the publisher's web site for a listing of these.

In summary, this work will prove a valuable addition to the bookshelves of Excel-oriented "number-crunchers." For those who prefer programs other than Excel, the examples can still provide useful instruction. For this group, the Excel material is of no use but also no real impediment. For those who hope to learn both data analysis and Excel at the same time, from "scratch," I doubt that this book will fill the bill: You'll probably need to start with more elementary treatises in both areas. I must admit that my aversion to the Excel program itself and its heavy focus in this book is what prevents me from giving the book the maximum rating.