Rohatagi, S., Hochhaus, G., Möllmann, H., Barth, J. Nippon Hoshasen Gijutsu Gakkai Zasshi 65, 306–311 (2009). Estimation of T1 and T2 using general-purpose spreadsheet software. Estimation of the protein-binding capacity of microplate wells using sequential ELISAs. Modeling transitions in body composition: the approach to steady state for anthropometric measures and physiological functions in the Minnesota human starvation study.
Biological modeling on a microcomputer using standard spreadsheet and equation solver programs: the hypothalamic-pituitary-ovarian axis as an example.
Modeling the micellization behavior of mixed and pure n-alkyl-maltosides. Additive action of two or more solutes on lipid membranes.
Excel linear regression with only certain points code#
Design and testing of a generalized reduced gradient code for nonlinear programming. Design and use of the Microsoft Excel Solver. (SYBEX, Alameda, 1995).įylstra, D., Lasdon, L., Watson, J. Excel for Scientists and Engineers 1st edn. Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting 2nd edn. 142–167 (McGraw-Hill Higher Education, New York, 2009). In Data Reduction and Error Analysis for the Physical Sciences 3rd edn. Least-squares fit to an arbitrary function. 656–706 (Cambridge University Press, New York, 1992).īevington, P.R. In Numerical Recipes in C: The Art of Scientific Computing 2nd edn. Press, W.H., Teukolsky, A.S., Vetterling, W.T. Why, when, and how biochemists should use least squares. (Oxford University Press, New York, 2008). Advanced Excel for Scientific Data Analysis 2nd edn. The entire protocol takes ∼1 h.ĭe Levie, R. Only slight modifications are required to adapt the protocol to virtually any other kind of dataset or regression equation. The application of this tool is exemplified using the well-known Michaelis–Menten equation characterizing simple enzyme kinetics. Every user familiar with the most basic functions of Excel will be able to implement this protocol, without previous experience in data fitting or programming and without additional costs for specialist software. The confidence of best-fit values is then visualized and assessed in a generally applicable and easily comprehensible way. Experimental data in x/ y form and data calculated from a regression equation are inputted and plotted in a Microsoft Excel worksheet, and the sum of squared residuals is computed and minimized using the Solver add-in to obtain the set of parameter values that best describes the experimental data. We describe an intuitive and rapid procedure for analyzing experimental data by nonlinear least-squares fitting (NLSF) in the most widely used spreadsheet program.
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