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Advanced Excel for Scientific Data Analysis | 
 enlarge | Author: Robert De Levie
Publisher: Oxford University Press, USA
Category: Book
List Price: $59.50
Buy New: $34.77
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Avg. Customer Rating:  7 reviews
Sales Rank: 212029
Media: Paperback
Edition: 2nd
Number Of Items: 1
Pages: 736
Shipping Weight (lbs): 2
Dimensions (in): 9.3 x 6.1 x 1.6
ISBN: 0195370228
Dewey Decimal Number: 507.27
EAN: 9780195370225
ASIN: 0195370228
Publication Date: August 14, 2008 (New: Last 30 Days)
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Shipping: International shipping available
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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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| Customer Reviews: Read 2 more reviews...
 VERY VERY HIGHLY RECOMMENDED!! August 27, 2008
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 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.
 A source of ideas on how Excel can be used in science September 11, 2006
2 out of 3 found this review helpful
This book does not give much info on Excel itself. I think the book is outstanding in that it opens one's eyes to using Excel for tasks like non-linear least squares fitting of data to models, signal deconvolution, etc. In retrospect, that one could use Excel for this should not be too surprising, but I have found myself resorting to MathCAD for many of these things when a solution implemented in Excel would have been easier to share with colleagues since Excel is more available. There is a bias towards biological/chemical examples, but nothing too egregious.
Excellent advanced manual for Excel users March 16, 2006
9 out of 9 found this review helpful
Every modern scientist and engineer relies upon some type of software for the analysis of data. Many software programs are available in the market today and each seems to have its own unique code and learning curve. In the PC world, perhaps no other software for data analysis is more common and easier to learn than Microsoft Excel. Many high school students are already using Excel for their homework assignments. All of these features make Excel an attractive analytical tool for scientists and engineers at university and afterwards. All such tools need reliable tutorials in order to train users to harness their full capabilities. Most available literature on Excel is introductory in nature, and therefore not appropriate for advanced applications. Robert de Levie's "Advanced Excel for scientific data analysis" helps fill in this void.
Prospective readers should be aware that this text is not appropriate for beginners. The author clearly alerts readers to this point in the preface. This is also readily apparent from browsing the Table of Contents. I was skeptical at first with some of the more advanced applications such as solving differential equations in Excel. Many scientists use higher-level programming languages such as Mathematica and Matlab to solve differential equations. While such software packages are quite powerful, they also have steep learning curves. I previously thought that Excel is not capable of solving differential equations, but Chapter 7 turned me into a believer.
The major emphasis of the examples is on least-squares and Fourier transformation. Chapter 2 does a nice job of contrasting Excel's three available routines for linear regression. The author does a very thorough job showing how Excel can be effectively used for Fourier transformation, and gives many examples. However, some other useful mathematical topics are either covered minimally or omitted entirely. For example, I was disappointed by the lack of a routine to calculate eigenvalues and eigenvectors. Excel's array structure makes it well-suited to linear algebra and the author should consider adding more on this topic in a future edition.
One of the greatest strengths of the book is its detailed coverage of Visual Basic for Applications (VBA). Advanced data analysis require the use of special user-defined functions, and VBA allows one to extend Excel capabilities to satisfy this need. Unfortunately, VBA code sometimes conflicts with Excel code. For example, the square root operation in Excel is SQRT, but in VBA is SQR. While the author certainly has no control over this, he does an excellent job alerting the reader to these pitfalls.
Chemists definitely need a reliable tool for the analysis of experimental data. de Levie's book covers most of the techniques we use in our lab. The book clearly demonstrates how Excel is not just a convenient tool for plotting data from the stock market or keeping track of students' grades, but a powerful tool for scientific data analysis. This book is highly rercommended for all students and research workers in the areas of analytical and physical chemistry.
Advanced Is Not Used Lightly in this Book's Title July 27, 2005
20 out of 20 found this review helpful
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.
Scientific number-crunching with Excel January 6, 2005
15 out of 16 found this review helpful
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.
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