ANISA NASWA LISTIANI, NIM. 17106010018 (2021) PERBANDINGAN METODE ALGORITMA SCHALL DAN BAYESIAN INFORMATION CRITERION (BIC) DALAM PEMILIHAN MODEL TERBAIK REGRESI RIDGE. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.
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Text (PERBANDINGAN METODE ALGORITMA SCHALL DAN BAYESIAN INFORMATION CRITERION (BIC) DALAM PEMILIHAN MODEL TERBAIK REGRESI RIDGE)
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Text (PERBANDINGAN METODE ALGORITMA SCHALL DAN BAYESIAN INFORMATION CRITERION (BIC) DALAM PEMILIHAN MODEL TERBAIK REGRESI RIDGE)
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Abstract
Multiple regression analysis is a statistical method that analyzes more than one independent variable againts a dependent variable. In the multiple regression analysis there are some assumptions that must be fulfilled. One of them is the absence of multicolinearity. Ridge regression is one of penalized regression models that can overcame multicolinearity. Ridge regression overcome multicolinearity by customize the least squares method of adds the bias conjucture, on other hand, the number of residual squared tends to be smaller than estimates gained by the least squares method, so a steady coefficient is obtained. There are some selection methods of ridge regression. In this research, Schall Algorithm and Bayesian Information Criterion (BIC) will be compared for selecting the best model ridge regression trough looking at MSE values and MAPE values. This research result is that the selection of the model using Schall Algorithm is more effective than BIC in the dataset of infant mortality in Central Java Province in 2019. The ridge regression model obtained from Schall’s Algorithm is : Y 0.4637PPM 0.0006KP 0.0005KN1 0.0000AE 0.0004PKB 0.0059BBLR
| Item Type: | Thesis (Skripsi) |
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| Additional Information: | Sri Utami Zuliana, S.Si., M.Sc., Ph.D |
| Uncontrolled Keywords: | Multiple Regressions, Multicolinearity, Ridge Regressions, Schall’s Algorithm, BIC, MSE, MAPE |
| Subjects: | Matematika |
| Divisions: | Fakultas Sains dan Teknologi > Matematika (S1) |
| Depositing User: | Drs. Mochammad Tantowi, M.Si. |
| Date Deposited: | 18 Nov 2021 09:26 |
| Last Modified: | 18 Nov 2021 09:26 |
| URI: | http://digilib.uin-suka.ac.id/id/eprint/46920 |
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