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138919

A new Method to Estimate the Parameters of Quadratic Regression

Article

Last updated: 23 Jan 2023

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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.

DOI

10.21608/djs.2019.138919

Keywords

Quadratic Regression, Kuhn-Tucker conditions, Autocorrelation

Authors

First Name

M.A.

Last Name

Kassem

MiddleName

-

Affiliation

Department of Mathematics, Faculty of Science, Tanta University

Email

mohd60_371@hotmail.com

City

-

Orcid

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First Name

A.M.

Last Name

Salem

MiddleName

-

Affiliation

Department of Mathematics, Faculty of Science, Tanta University, Tanta, Egypt

Email

anmsalem45@yahoo.com

City

-

Orcid

-

First Name

N.G.

Last Name

Ragab

MiddleName

-

Affiliation

Department of Mathematics, Faculty of Science, Tanta University, Tanta, Egypt

Email

-

City

-

Orcid

-

Volume

40

Article Issue

1

Related Issue

20595

Issue Date

2019-06-01

Receive Date

2021-01-12

Publish Date

2019-06-01

Page Start

24

Page End

29

Print ISSN

1012-5965

Online ISSN

2735-5306

Article File

DJS_Volume 40_Issue 1_Pages 24-29.pdf

PDF . 961.3kB

Link

https://djs.journals.ekb.eg/article_138919.html

Detail API

https://djs.journals.ekb.eg/service?article_code=138919

Order

3

Type

Research and Reference

Type Code

1,686

Publication Type

Journal

Publication Title

Delta Journal of Science

Publication Link

https://djs.journals.ekb.eg/

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Details

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Article

Created At

23 Jan 2023