A new Method to Estimate the Parameters of Quadratic Regression | ||||
Delta Journal of Science | ||||
Article 3, Volume 40, Issue 1, June 2019, Page 24-29 PDF (938.82 K) | ||||
Document Type: Research and Reference | ||||
DOI: 10.21608/djs.2019.138919 | ||||
View on SCiNiTO | ||||
Authors | ||||
M.A. Kassem 1; A.M. Salem2; N.G. Ragab2 | ||||
1Department of Mathematics, Faculty of Science, Tanta University | ||||
2Department of Mathematics, Faculty of Science, Tanta University, Tanta, Egypt | ||||
Abstract | ||||
In this study, a new method to estimate the parameters for a quadratic regression model is introduced by using Kuhn-Tucker conditions. Kuhn-Tucker conditions provide the minimizing error of the estimated parameters for a quadratic regression. This method can be used for any data set of a quadratic regression, and we discuss the test for correct specification of disturbances mainly because of their ability to detect the irregularities in the regressor specification. | ||||
Keywords | ||||
Quadratic Regression; Kuhn-Tucker conditions; Autocorrelation | ||||
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