International Journal of Applied Research
Vol. 2, Issue 12, Part H (2016)
The effect of multicollinearity in nonlinear regression models
P Kesavulu, Vasu K, M Bhupathi Naidu, R Abbaiah and P Balasiddamuni
Regression Analysis having many procedures for modeling and analyzing the relationship between a dependent variable and one or more explanatory variables. Linear and nonlinear regression models have extensively used in many fields of business science. One of the impartment problems in regression Analysis is multicollinearity between the explanatory variables. If there is no linear relationship between the explanatory variables, they are said to be orthogonal model. In the case of orthogonal variables, statistical assumption on the model is relatively reliable. But in day today life, fully unbound variables which are explaining the dependent variable are likely to be very low. When the explanatory variables are not orthogonal, then least squares parameter estimation method will not provide an appropriate junction, and deviations from reality will occur.
How to cite this article:
P Kesavulu, Vasu K, M Bhupathi Naidu, R Abbaiah and P Balasiddamuni. The effect of multicollinearity in nonlinear regression models. International Journal of Applied Research. 2016; 2(12): 506-509.