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