Estimasi Parameter Regresi Linear Menggunakan Regresi Kuantil

Rachmawati, Baiq Devi (2018) Estimasi Parameter Regresi Linear Menggunakan Regresi Kuantil. S1 thesis, Universitas Mataram.

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Regression analysis is a statistical analysis method for estimating the relationship between dependent variables Y) and one or more independent variables X . As the purpose of this study is to theoretically examine the quantile egression method in estimating linear regression parameters. In regression analysis usually the method used to estimate arameters is the least square method with assumptions that must be met that normal assumption, homoskedasticity, no utocorrelation and non multicollinearity. Basically the least square method is sensitive to the assumptions of deviations n the data, so that the estimations results will be lees good if the assumptions are not fulfilled. Therefore, to overcome the mitations of the least square method developed a quantile regression method for estimating linear regression parameters. Based on the result of research that has been done shows that the estimation of linear regression parameters using the uantile regression method is obtained by minimazing the absolute number of errors through the simplex algorithm.

Item Type: Thesis (S1)
Keywords (Kata Kunci): Least square method, Quantile regression method, Simplex algorithm.
Subjects: S Agriculture > S Agriculture (General)
Divisions: Fakultas Matematika dan ilmu Pengetahuan Alam
Depositing User: Saprudin Saprudin
Date Deposited: 27 Nov 2018 05:59
Last Modified: 27 Nov 2018 05:59

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