ENGGUNAAN METODE ELIMINASI GAUSS UNTUK MENCARI NILAI KOEFISIEN-KOEFISIEN PADA GARIS REGRESI LINIER BERGANDA
| Main Author: | OKKY ELANDA. D, BIANCA |
|---|---|
| Format: | Thesis NonPeerReviewed |
| Terbitan: |
, 2013
|
| Subjects: | |
| Online Access: |
http://eprints.umm.ac.id/15771/ |
Daftar Isi:
- Multiple linear regression analysis was used to predict analytical value of the dependent variable Y when the independent variable x amount to two or more. Then to get a multiple linear regression model can be obtained by estimating the parameters using the least squares method (ordinary least square / OLS). Within a regression model coefficients will be found that a predictive value in the parameter regression model. Linear equation is an equation that if the curve is a straight line drawn. While the system of linear equations is a system that involves a series of at least two linear equations. Resolve linear equations or look for solutions of systems of linear equations as well as the search for the point of intersection between the linear equations are known. One method used to find the solution of this linear system of equations is Gaussian elimination method. Finding the value of the coefficients in the linear regression line is the same as finding a solution of the system of linear equations formed through a process of normal equations method of least squares linear regression equation. Search this solution using Gaussian elimination method. The procedure of this method is based on the idea of reducing the augmented matrix of a system into row echelon form of a matrix. This method departs from the fact that when an upper triangular matrix A then, the solution can be calculated with the technique backward substitution (backward-substitution). In principle, the Gaussian elimination method aims to transform the system Ax = b be a system Ux = y, with U is an upper triangular matrix by rules triangulation (Elementary Row Operations). Furthermore the solution x can be calculated by backward penyulihan techniques.