National Exam (UN) is one indicator of educational quality improvement. The ...
SMP, tryout score, rapor score, competency test score, the number of siblings,.
APPLICATION SEMIPARAMETRIC SPLINE REGRESSION ON MODELLING UN SCORE OF SMKN 1 NGULING PASURUAN STUDENTS Name NRP Supervisor
: Winarti Purwahyuningsih : 1308201017 : Dr. Sony Sunaryo, M.Si
ABSTRACT National Exam (UN) is one indicator of educational quality improvement. The variables that affect the average score of UN SMK, namely average score of UN SMP, tryout score, rapor score, competency test score, the number of siblings, distance from home to school and parental income is used as a predictor variable. Regression analysis was used to observe the effect of predictor variables on response variables with the first look at the pattern of relations between the two variables. This can be done through two approaches, the most common approach and is often used is the parametric approach which assumes that the shapes of regression functions are known and if there is no any information about the functional form of regression approaches is nonparametric approach. If both approaches are combined is formed semiparametric approach. Estimation technique Semiparametric regression used is the spline, because the spline has the advantage, among others, can overcome the data patterns that indicate a change in behavior in certain sub-units with the help of knot points. The purpose of this study is to assess estimator semiparametric multivariable spline regression model using Least Square dan memilih model regresi spline terbaik and choose the best spline regression model with Generalized Cross Validation (GCV) and Mean Square Error (MSE) and its application in data score National Examination (UN) with the help of the program S-Plus. The data used are the average data score UN SMKN 1 Nguling Pasuruan against the average score of UN SMP, the tryout, rapor score, score test of competence, number of siblings, distance from home to school and parental income. The results showed that the best spline regression model is multivariable spline regression knot point mix, with minimum GCV of 0.05270084, the MSE of 0.04851492, and the coefficient of determination (R2) equal to 80.15%. Testing the model explains that with a significance level of 5% of the average score of tryout, the average rapor score, the average score of UN SMP, the average score of the competency test, number of siblings, parental income and home to school distance affect significantly against the average score of UN SMKN 1 Nguling Pasuruan.
Keywords: Generalized Cross Validation (GCV), Least Square Mean Square Error (MSE), semiparametric regression, Spline, Knot Point.
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