Pemodelan Regresi Nonparametrik Dengan B – Spline Dan Mars
| Main Author: | Mahdia, Sarah |
|---|---|
| Other Authors: | Aris, Suwarno, Sembiring, Pasukat |
| Format: | Student Papers |
| Bahasa: | ind |
| Subjects: | |
| Online Access: |
http://repository.usu.ac.id/handle/123456789/29996 |
Daftar Isi:
- Regression analysis is constructed for capturing the influences of independent variables to dependent ones. It can be done by looking at the relationship between those variables. This task of approximating the mean function can be done essentially in two ways. The quiet often use parametric approach is to assume that the mean curve has some prespecified functional forms. Alternatively, nonparametrik approach, .i.e., without reference to a specific form, is used when there is no information of the regression function form (Hardle, 1990). Therefore nonparametric approach has more flexibilities than the parametric on. The aim of this research is to find the best fit model that captures relationship between admission test score to the GPA. The purpose of this research is to obtain the best model abaout mark on midterm against final exam score student majoring in industrial chemistry second 2009/2010 academic year,Faculty of Mathematics and Natural Science northem Sumatera with a simple linear, quadratic,and cubic regression analysis. Those two approaches were used here. In the parametric approach, we use simple linear, quadric cubic regression, and in the nonparametric ones, we use B – Spline and Multivariate Adaptive Regression Splines (MARS). Overall, the best model was chosen based on the maximum determinant coefficient. However, for MARS, the best model was chosen based on the GCV, minimum MSE, maximum determinant coefficient.
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