MODEL REGRESI NONPARAMETRIK B-SPLINE (STUDI KASUS: PERSENTASE KEMISKINAN DAN FAKTOR-FAKTOR YANG MEMPENGARUHI PERSENTASE PENDUDUK MISKIN PROVINSI JAWA BARAT TAHUN 2017)

Risma Nur Sukmawati, NIM.: 15610026 (2019) MODEL REGRESI NONPARAMETRIK B-SPLINE (STUDI KASUS: PERSENTASE KEMISKINAN DAN FAKTOR-FAKTOR YANG MEMPENGARUHI PERSENTASE PENDUDUK MISKIN PROVINSI JAWA BARAT TAHUN 2017). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Regression analysis became the basis for conclusion (inferencing) about the functional relationship between predictor variables and response variable. In regression analysis there are two approaches, called parametric approach and nonparametric approach. A nonparametric approach is not banded by normality assumption and the data given as independence of search the curve regression so this approach are very flexibility and objective. Nonparametric regression approaches that are often used include spline regression, which is one type of polynomial piecewise, a polynomial that has a nature segmented. Spline approach has a basic function that commonly used, which is B-Spline. Data in this research is poverty percentage regencies / cities of West Java Province in 2017. As dependent variable is the percentage of the poor people as Y and independent variables are the open unemployment rate as 1 X and the average length of school as 2 X . The best B-Spline regression model for estimating the percentage data of the poor people in West Java using the Weighted Least Square method is : Yˆ             1,2 1 0,2 1 1,2 1 1,2 2 0,2 2 1,2 2 0,9825 ( ) 3,3417 ( ) 0,9575 ( ) 11,9249 ( ) 10,1563 ( ) 4,0192 ( ) N x N x N x N x N x N x with the MSE value is 3,9776 then determination value is 58.05% and the GCV value is 5.9866.

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