scholarly journals The Use of Non-Linear Regression Methods for Analysing Sensitivity and Quantal Response Data

1965 ◽  
Author(s):  
R Moore ◽  
R Zeigler
Keyword(s):  
Linear Regression ◽  
Quantal Response ◽  
Regression Methods ◽  
Response Data ◽  
Non Linear

Modeling of phenol adsorption isotherm onto activated carbon by non-linear regression methods: models with three and four parameters

2018 ◽  
Vol 136 ◽  
pp. 199-206
Author(s):  
Donald Raoul Tchuifon Tchuifon ◽  
George Nche Ndifor-Angwafor ◽  
Aurelien Bopda ◽  
Solomon Gabche Anagho
Keyword(s):  
Activated Carbon ◽  
Adsorption Isotherm ◽  
Linear Regression ◽  
Phenol Adsorption ◽  
Regression Methods ◽  
Non Linear

scholarly journals Progress-curve equations for reversible enzyme-catalysed reactions inhibited by tight-binding inhibitors

1990 ◽  
Vol 265 (3) ◽  
pp. 647-653 ◽  
Author(s):  
S E Szedlacsek ◽  
V Ostafe ◽  
R G Duggleby ◽  
M Serban ◽  
M O Vlad
Keyword(s):  
Linear Regression ◽  
Competitive Inhibition ◽  
Tight Binding ◽  
The Other ◽  
First Order ◽  
Regression Methods ◽  
Progress Curve ◽  
Dead End ◽  
Non Linear ◽  
Inhibition Constants

The rate equation for a tight-binding inhibitor of an enzyme-catalysed first-order reversible reaction was used to derive two integrated equations. One of them covers the situations in which competitive, uncompetitive or non-competitive inhibition occurs and the other refers to the special non-competitive case where the two inhibition constants are equal. For these equations, graphical and non-linear regression methods are proposed for distinguishing between types of inhibition and for calculating inhibition constants from progress-curve data. The application of the non-linear regression to the analysis of stimulated progress curves in the presence of a tight-binding inhibitor is also presented. The results obtained are valid for any type of ‘dead-end’-complex-forming inhibitor and can be used to characterize an unknown inhibitor on the basis of progress curves.

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