The Experts below are selected from a list of 116676 Experts worldwide ranked by ideXlab platform
Henry Wai Chau - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of parameter uncertainties in nonlinear regression using Microsoft Excel Spreadsheet
Environmental Systems Research, 2015Co-Authors: Jing Xie, Henry Wai ChauAbstract:Nonlinear relationships are common in the environmental discipline. Spreadsheet packages such as Microsoft Excel come with an add-on for nonlinear regression, but parameter uncertainty estimates are not yet available. The purpose of this paper is to use Monte Carlo and bootstrap methods to estimate nonlinear parameter uncertainties with a Microsoft Excel spreadsheet. As an example, uncertainties of two parameters (α and n) for a soil water retention curve are estimated. The fitted parameters generally do not follow a normal distribution. Except for the upper limit of α using the bootstrap method, the lower and upper limits of α and n obtained by these two methods are slightly greater than those obtained using the SigmaPlot software which linearlizes the nonlinear model. Since the linearization method is based on the assumption of normal distribution of parameter values, the Monte Carlo and bootstrap methods may be preferred to the linearization method.
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Evaluation of parameter uncertainties in nonlinear regression using Microsoft Excel Spreadsheet
Environmental Systems Research, 2015Co-Authors: Jing Xie, Henry Wai ChauAbstract:Background Nonlinear relationships are common in the environmental discipline. Spreadsheet packages such as Microsoft Excel come with an add-on for nonlinear regression, but parameter uncertainty estimates are not yet available. The purpose of this paper is to use Monte Carlo and bootstrap methods to estimate nonlinear parameter uncertainties with a Microsoft Excel spreadsheet. As an example, uncertainties of two parameters ( α and n ) for a soil water retention curve are estimated. Results The fitted parameters generally do not follow a normal distribution. Except for the upper limit of α using the bootstrap method, the lower and upper limits of α and n obtained by these two methods are slightly greater than those obtained using the SigmaPlot software which linearlizes the nonlinear model. Conclusions Since the linearization method is based on the assumption of normal distribution of parameter values, the Monte Carlo and bootstrap methods may be preferred to the linearization method.
Leo Knusel - One of the best experts on this subject based on the ideXlab platform.
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On the reliability of Microsoft Excel XP for statistical purposes
Computational Statistics & Data Analysis, 2002Co-Authors: Leo KnuselAbstract:The paper deals with the reliability of Microsoft Excel XP for statistical purposes. In section 1 we show that Excel can compute negative variances. In section 2 we see that the random numbers generated by Excel cannot suffice scientific statistical requirements, and in section 3 we have to report that unbearable errors with the computation of statistical distributions that have been reported to Microsoft and later on have been published (Knusel, 1998) are still to be found in Microsoft Excel XP. So one has again to warn the scientific community against using Microsoft Excel for statistical purposes.
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on the accuracy of statistical distributions in Microsoft Excel 97
Computational Statistics & Data Analysis, 1998Co-Authors: Leo KnuselAbstract:Some of previously indicated errors in Microsoft Excel 97 and Excel XP have been eliminated in Excel 2003. But some others have not been corrected in Excel 2003 and new ones have been found as is shown by numerical examples.
Jing Xie - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of parameter uncertainties in nonlinear regression using Microsoft Excel Spreadsheet
Environmental Systems Research, 2015Co-Authors: Jing Xie, Henry Wai ChauAbstract:Nonlinear relationships are common in the environmental discipline. Spreadsheet packages such as Microsoft Excel come with an add-on for nonlinear regression, but parameter uncertainty estimates are not yet available. The purpose of this paper is to use Monte Carlo and bootstrap methods to estimate nonlinear parameter uncertainties with a Microsoft Excel spreadsheet. As an example, uncertainties of two parameters (α and n) for a soil water retention curve are estimated. The fitted parameters generally do not follow a normal distribution. Except for the upper limit of α using the bootstrap method, the lower and upper limits of α and n obtained by these two methods are slightly greater than those obtained using the SigmaPlot software which linearlizes the nonlinear model. Since the linearization method is based on the assumption of normal distribution of parameter values, the Monte Carlo and bootstrap methods may be preferred to the linearization method.
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Evaluation of parameter uncertainties in nonlinear regression using Microsoft Excel Spreadsheet
Environmental Systems Research, 2015Co-Authors: Jing Xie, Henry Wai ChauAbstract:Background Nonlinear relationships are common in the environmental discipline. Spreadsheet packages such as Microsoft Excel come with an add-on for nonlinear regression, but parameter uncertainty estimates are not yet available. The purpose of this paper is to use Monte Carlo and bootstrap methods to estimate nonlinear parameter uncertainties with a Microsoft Excel spreadsheet. As an example, uncertainties of two parameters ( α and n ) for a soil water retention curve are estimated. Results The fitted parameters generally do not follow a normal distribution. Except for the upper limit of α using the bootstrap method, the lower and upper limits of α and n obtained by these two methods are slightly greater than those obtained using the SigmaPlot software which linearlizes the nonlinear model. Conclusions Since the linearization method is based on the assumption of normal distribution of parameter values, the Monte Carlo and bootstrap methods may be preferred to the linearization method.
Knüselleo - One of the best experts on this subject based on the ideXlab platform.
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On the reliability of Microsoft Excel XP for statistical purposes
Computational Statistics & Data Analysis, 2002Co-Authors: KnüselleoAbstract:The paper deals with the reliability of Microsoft Excel XP for statistical purposes. In section 1 we show that Excel can compute negative variances. In section 2 we see that the random numbers gene...
Bernard V Liengme - One of the best experts on this subject based on the ideXlab platform.
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Modelling Physics with Microsoft Excel
2014Co-Authors: Bernard V LiengmeAbstract:The purpose of this work is to show some of the ways in which Microsoft Excel may be used to solve numerical problems in the field of physics. But why use Excel in the first place? Certainly Excel is never going to out-perform the wonderful symbolic algebra tools that we have today—Mathematica. Mathcad, Maple, MATLAB, etc. However, from a pedagogical stance Excel has the advantage of not being a 'black box' approach to problem solving. The user must do a lot more work than just call up a function. The intermediate steps in a calculation are displayed on the worksheet—of course this is not true with the Solver add-in which is a wonderful 'black box'. Another advantage is the somewhat less steep learning curve. A high school student can quickly learn how to get Excel to do useful calculations.
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Guide to Microsoft Excel 2007 for Scientists and Engineers
2008Co-Authors: Bernard V LiengmeAbstract:Completely updated guide for scientists, engineers and students who want to use Microsoft Excel 2007 to its full potential.Electronic spreadsheet analysis has become part of the everyday work of researchers in all areas of engineering and science. Microsoft Excel, as the industry standard spreadsheet, has a range of scientific functions that can be utilized for the modeling, analysis and presentation of quantitative data. This text provides a straightforward guide to using these functions of Microsoft Excel, guiding the reader from basic principles through to more complicated areas such as formulae, charts, curve-fitting, equation solving, integration, macros, statistical functions, and presenting quantitative data. Key Features:* Content written specifically for the requirements of science and engineering students and professionals working with Microsoft Excel, brought fully up to date with the new Microsoft Office release of Excel 2007. * Features of Excel 2007 are illustrated through a wide variety of examples based in technical contexts, demonstrating the use of the program for analysis and presentation of experimental results. * Updated with new examples, problem sets, and applications. New website with data sets, downloadable spreadsheets and other useful resources.
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a guide to Microsoft Excel for scientists and engineers
1997Co-Authors: Bernard V LiengmeAbstract:From the Publisher: This book provides scientists and engineers with an introductory look at the tools available to them in Microsoft Excel. It provides valuable information on the more technical functions of Microsoft Excel in a user friendly style.
