Advanced Excel for Scientific Data Analysisby Robert de Levie
Excel is by far the most widely distributed data analysis software but few users are aware of its full powers. Advanced Excel For Scientific Data Analysis takes off from where most books dealing with scientific applications of Excel end. It focuses on three areas-least squares, Fourier transformation, and digital simulation-and illustrates these with extensive
Excel is by far the most widely distributed data analysis software but few users are aware of its full powers. Advanced Excel For Scientific Data Analysis takes off from where most books dealing with scientific applications of Excel end. It focuses on three areas-least squares, Fourier transformation, and digital simulation-and illustrates these with extensive examples, often taken from the literature. It also includes and describes a number of sample macros and functions to facilitate common data analysis tasks. These macros and functions are provided in uncompiled, computer-readable, easily modifiable form; readers can therefore use them as starting points for making their own personalized data analysis tools. Detailed descriptions and sample applications of standard and specialized uses of least squares for fitting data to a variety of functions, including resolving multi-component spectra; standard processes such as calibration curves and extrapolation; custom macros for general "error" propagation, standard deviations of Solver results, weighted or equidistant least squares, Gram-Schmidt orthogonalization, Fourier transformation, convolution and deconvolution, time-frequency analysis, and data mapping. There are also worked examples showing how to use centering, the covariance matrix, imprecision contours, and Wiener filtering and custom functions for bisections, Lagrange interpolation, Euler and Runge-Kutta integration.
- Oxford University Press, USA
- Publication date:
- Edition description:
- New Edition
- Product dimensions:
- 9.10(w) x 6.10(h) x 1.30(d)
Meet the Author
Robert de Levie is the author of more than 160 papers in analytical chemistry and electrochemistry, of an early Spreadsheet Workbook for Quantitative Chemical Analysis, McGraw-Hill, 1992; of a textbook on the Principles of Quantitative Chemical Analysis, McGraw-Hill 1997; of an Oxford Chemistry Primer on Aqueous Acid-Base Equilibria and Titrations, Oxford University Press, 1999; and most recently, of How to Use Excel in Analytical Chemistry, Cambridge University Press, 2001. He was born and raised in the Netherlands, earned his Ph.D. in physical chemistry at the University of Amsterdam, was a postdoctoral fellow with Paul Delahay in Baton Rouge, Louisiana, and for 34 years taught analytical chemistry and electrochemistry at Georgetown University. For ten of those years, he was the US editor of the Journal of Electroanalytical Chemistry. Now an emeritus professor, he lives on Orr's Island, and is associated with Bowdoin College in nearby Brunswick, Maine.
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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.