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The next table shows the results for the model parameters. In our case, 99% of the variability is explained by the two variables and their interaction, which is an excellent result that confirms that the selected model is appropriate. The closer to 1 the R² is, the better the fit. The R² corresponds to the % of the variability of the dependent variable (the viscosity) that is explained by the two explanatory variables (the components). The second table (see below) displays the goodness of fit coefficients, including the R² (coefficient of determination), and the SSE (sum of square of errors), the latter being the criterion used for the model optimization. The first table gives the basic statistics for the selected variables. Interpreting the results of a nonlinear multiple regression The computations begin once you have clicked on the OK button. Click Save to add the model to the library of user-defined functions. Check the Derivatives option and select their values from the Excel sheet. Click Add and enter the equation F (C1, C2) of our model. We can either choose one of the build-in functions (see below) or define our own function. In the Functions tab, we have to possibilities. In the Options tab, we select the values of the initial values of the five parameters. We select the Residuals option as well in order to analyze the predictions and the residuals. The quantitative explanatory variables are the concentration of the two components C1and C2.Īs we selected the column headers, we need to activate the option Variable labels. The Dependent variable (or response variable) is in our case the Viscosity.
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The nonlinear regression dialog box pops up. Setting up a nonlinear multiple regressionĪfter opening XLSTAT, select the XLSTAT / Modeling data / Nonlinear regression feature. This logistic-like model allows taking into account both the concentrations of the components and their interaction. Our purpose is to study the effect of the concentration of two components, C1 and C2, on the viscosity of a yogurt.
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Nonlinear regression is used to model complex phenomena which cannot be handled by linear models. Dataset to run a nonlinear multiple regression
#Linear regression using excel in office 365 video tutorial how to#
This tutorial explains how to set up and interpret a multiple nonlinear regression in Excel with XLSTAT.