Non linear multiple regression with XLSTAT

Dataset to run a non linear multiple regression

An Excel sheet with both the data and the results can be downloaded by clicking here. Our purpose is to study the effect of the concentration of two components, C1 and C2, on the viscosity of a yogurt. The model that we want to fit writes:

F(C1, C2) = pr5 / (1+Exp(-pr1-pr2*C1-pr3*C2-pr4*C1*C2))

pr1, ..., pr5 are the parameters of the model. This logistic-like model allows to take into account both the concentrations of the components and the interaction between them.

Setting up a non linear multiple regression

After opening XLSTAT, select the XLSTAT / Modeling data / Nonlinear regression command, or click on the corresponding button of the Modeling Data toolbar (see below).

Once you've clicked on the button, the nonlinear regression dialog box appears. Select the data on the Excel sheet.

The Dependent variable (or response variable) is in our case the "Viscosity".

The quantitative explanatory variables are the concentration of the two components "C1" and "C2".

As we selected the column headers, we left the option Variable labels option activated. We left the Residuals option activated as well, because we want to analyze the predictions and the residuals.

In the Options tab we selected the values of the initial values of the five parameters.

In the Functions tab, the various functions are displayed. As the function we want to use is not listed in the Preprogrammed functions (you can notice the univariate version of the function in the list), we needed to enter the model: we first clicked on Add, then entered the function, then checked Derivatives, then selected them on the Excel sheet. In order to add this function to the user functions library, we clicked on Save. The function is then automatically added and selected.

The computations begin once you have clicked on the OK button. The results will then be displayed.

Interpreting the results of a non linear multiple regression

The first table gives the basic statistics of the selected variables.

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 later being the criterion used for the model optimization. The R² corresponds to the % of the variability of the dependant variable (the viscosity) that is explained by the two explanatory variables (the components). The closer to 1 the R² is, the better the fit.

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 next table shows the results for the model parameters. As we can see, the ratios (parameter)/(std deviation) are larger for pr5 and pr4. As the same ratio is the largest for pr5 we deduce that the interaction between the two components has a greater effect on the viscosity than the concentrations themselves.

The following chart allows to visualize the quality of the fit by comparing the predicted values to the observed values.

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