ANALISIS REGRESI RIDGE DUA TAHAP UNTUK PERMASALAHAN MULTIKOLINEARITAS
Regression analysis is a statistical analysis that used to perform model relationship between dependent variable and independent variable. One of the assumption in classical regression analysis is there is no multicollinearity problem. If there is multicollinearity in the regression model, it could...
Saved in:
| Main Authors: | , |
|---|---|
| 格式: | Theses and Dissertations NonPeerReviewed |
| 出版: |
[Yogyakarta] : Universitas Gadjah Mada
2013
|
| 主題: | |
| 在線閱讀: | https://repository.ugm.ac.id/124008/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=64126 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | Regression analysis is a statistical analysis that used to perform model
relationship between dependent variable and independent variable. One of the
assumption in classical regression analysis is there is no multicollinearity
problem. If there is multicollinearity in the regression model, it could cause the
results of model that using the method of Least Squares estimator becomes
invalid. Over the years, there are a lot of variety modern regression analysis. And
one of the modern regression analysis that can overcome the multicollinearity
problem is the Ridge regression analysis. Ridge regression analysis was first
introduced by A.E Hoerl and Kennard in 1970. Two Stages Ridge regression
analysis recently introduced by Hussein Eledum and Mostafa Zahri in 2013. Two
Stages Ridge Regression analysis method is a combination of Two Stage Least
Squares and Ordinary Ridge Regression.
In this paper, two stages of Ridge regression analysis was applied to the
analysis of the factors that affect the amount of money circulating in the U.S. to
obtain the model that free from multicollinearity problem. |
|---|