A new Method to Estimate the Parameters of Quadratic Regression
Kassem, M., Salem, A., Ragab, N. (2019). A new Method to Estimate the Parameters of Quadratic Regression. EKB Journal Management System, 40(1), 24-29. doi: 10.21608/djs.2019.138919
M.A. Kassem; A.M. Salem; N.G. Ragab. "A new Method to Estimate the Parameters of Quadratic Regression". EKB Journal Management System, 40, 1, 2019, 24-29. doi: 10.21608/djs.2019.138919
Kassem, M., Salem, A., Ragab, N. (2019). 'A new Method to Estimate the Parameters of Quadratic Regression', EKB Journal Management System, 40(1), pp. 24-29. doi: 10.21608/djs.2019.138919
Kassem, M., Salem, A., Ragab, N. A new Method to Estimate the Parameters of Quadratic Regression. EKB Journal Management System, 2019; 40(1): 24-29. doi: 10.21608/djs.2019.138919

A new Method to Estimate the Parameters of Quadratic Regression

Delta Journal of Science
Article 3, Volume 40, Issue 1, June 2019, Pages 24-29    PDF (938.82 K)
Document Type: Research and Reference
DOI: 10.21608/djs.2019.138919
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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