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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