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ISSN Print: 2394-7500, ISSN Online: 2394-5869, CODEN: IJARPF

Impact Factor: RJIF 8.4

International Journal of Applied Research

Vol. 2, Issue 12, Part H (2016)

The effect of multicollinearity in nonlinear regression models

Author(s)
P Kesavulu, Vasu K, M Bhupathi Naidu, R Abbaiah and P Balasiddamuni
Abstract
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.
Pages: 506-509  |  690 Views  28 Downloads
How to cite this article:
P Kesavulu, Vasu K, M Bhupathi Naidu, R Abbaiah, P Balasiddamuni. The effect of multicollinearity in nonlinear regression models. Int J Appl Res 2016;2(12):506-509.
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