I have evaluated the adsorption and desorption of two pollutants from aqueous solutions in singular and binary conditions with three different adsorbents.
In my adsorption dataset, there are four inputs, which include Initial concentration, adsorbents dosage, contact time, and temperature. Among them, only the Initial concentration of pollutants is variable and the rest of the environmental conditions are constant for all the samples.
Regarding the explained condition, is it possible and rational run a linear or non-linear regression model for my dataset?
Dear Meysam Shahrashoub ,
I agree with Rabin Thapa, you can run a linear regression predicting adsorption and desorption (are these two variables?) by the use of initial dosage as predictor. I wondered however, why you did not want to include the adsorbent (you said you had three different) as additional predictor.
Best
Sven
Most nonlinear models have one continuous independent variable, but it is possible to have more than one. When you have one independent variable, you can graph the results using a fitted line plot.
Use a scatter plot diagram and fit the linear regression model and explain via R2 value...since we have only one independent variable.
Dear Meysam Shahrashoub ,
I agree with Rabin Thapa, you can run a linear regression predicting adsorption and desorption (are these two variables?) by the use of initial dosage as predictor. I wondered however, why you did not want to include the adsorbent (you said you had three different) as additional predictor.
Best
Sven
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