ANALISIS REGRESI NONPARAMETRIK DENGAN METODE CUBIC SPLINE DAN B-SPLINE (STUDI KASUS: FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI INDONESIA PADA TAHUN 2021-2022)

Rila Handini Agestia, NIM.: 19106010006 (2023) ANALISIS REGRESI NONPARAMETRIK DENGAN METODE CUBIC SPLINE DAN B-SPLINE (STUDI KASUS: FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI INDONESIA PADA TAHUN 2021-2022). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Regression analysis is a method used to show the effect of the predictor variable (independent variable) on the response variable (dependent). The approach to regression analysis is divided into parametric and non-parametric, where the parametric approach requires several assumptions that must be fulfilled such as data with an error distribution, free from multicollinearity and autocorrelation, and heteroscedasticity. However, in everyday life the data required sometimes does not match the assumptions. So it requires more flexible approach, such as nonparametric approach. Cubic Spline is one of the methods commonly used in the nonparametric approach. Selection of many and adjacent knot points can result in singular calculations, so basis for the B-Spline function is needed. The dependent variable used is the value of the Human Development Index in 2021-2022 with the independent variables used being the Average Length of School and Adjusted Per Capita Spending. Determination of the optimal knot point uses the smallest GCV. Selection of the best model uses the smallest GCV and MSE values. The best model obtained is: 2 1 1 ˆ 60.25004 Y   75.97957X  70.13013X  3 3 1 1 +74.18333X 81.58604 X 7.060601 

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