A parametric regression approach is used when the shape of the regression curve is known, and the nonparametric approach is used when the shape of the regression curve is unknown, the parametric regression model is still forced as a model of data patterns, it will cause inaccurate conclusions if the form of the function is not known. The truncated spline approach can be used to estimate curves in nonparametric regression models using a knot basis. The curve estimation method used is Weighted Least Square (WLS) for the truncated spline approach. In this study, the nonparametric regression model was implemented on fertilizer subsidy data in East Java. This study aims to examine comprehensively the factors that influence the satisfaction of the farming community. The response variable used in this study is the Courage of Field Extension Officers (Y), while the predictor variables include: Farmers National Culture (X1), Financial Reward for Field Extension Officers (X2), and Leadership Role (X3). The novelty of this research is trying to do regression modeling in which the whole relationship between the response variable and the predictor variable is non-linear. The results of the linearity assumption test show that all predictor variables show a non-linear relationship to the response variable, so it needs to be solved by nonparametric regression. The coefficient of determination shows that the variability of Courage of Field Extension Officers can be explained by 87,65% in the model, while the remaining 12,35% is explained by factors that are not included in the model.
Regression analysis has three approaches in estimating the regression curve, namely: parametric, nonparametric, and semiparametric approaches. Several studies have discussed modeling with the three approaches in cross-section data, where observations are assumed to be independent of each other. In this study, we propose a new method for estimating parametric, nonparametric, and semiparametric regression curves in spatial data. Spatial data states that at each point of observation has coordinates that indicate the position of the observation, so between observations are assumed to have different variations. The model developed in this research is to accommodate the influence of predictor variables on the response variable globally for all observations, as well as adding coordinates at each observation point locally. Based on the value of Mean Square Error (MSE) as the best model selection criteria, the results are obtained that modeling with a nonparametric approach produces the smallest MSE value. So this application data is more precise if it is modeled by the nonparametric truncated spline approach. There are eight possible models formed in this research, and the nonparametric model is better than the parametric model, because the MSE value in the nonparametric model is smaller. As for the semiparametric regression model that is formed, it is obtained that the variable X 2 is a parametric component while X 1 and X 3 are the nonparametric components (Model 2). The regression curve estimation model with a nonparametric approach tends to be more efficient than Model 2 because the linearity assumption test results show that the relationship of all the predictor variables to the response variable shows a non-linear relationship. So in this study, spatial data that has a non-linear relationship between predictor variables and responses tends to be better modeled with a nonparametric approach.
“Jelantah” is cooking palm oil which is used repeatedly. Its daily use leads to enhancement of free radical level in the body. Free radical level should be lowered by a compound named antioxidant, either synthetic or natural antioxidant. This study aims to analyze the effect of P. speciosa peel ethanolic extract (PSPE) to superoxide dismutase (SOD) and malondialdehyde (MDA) level of jelantah exposed Rattus norvegicus (rats). The rats were divided into four groups. There were group I as a negative control (without jelantah and PSPE treatment), group II with 1 ml jelantah 118 mek/kg + 100 mg/kg body weight PSPE treatment, group III with 1 ml jelantah 118 mek/kg + 200 mg/kg body weight PSPE treatment, and group IV is a positive control (with 1 ml jelantah 118 mek/kg). The results showed that range of SOD and MDA level are 20.63-79.06 U/ml and 1.75-9.34 nmol/ml, respectively, with significantly different at α: 0.05. The negative control showed the highest SOD level and lowest MDA level because it was not treated with jelantah. On the other hand, the positive control showed the opposite because it was only treated with jelantah without addition of PSPE. Furthermore, the group III showed higher SOD and lower MDA level than group II. Those indicate that the higher PSPE concentration, the higher SOD level and the lower MDA level. SOD as an antioxidant has contrary level to MDA as free radical. PSPE showed a significant effect to the enhancement of SOD level and the reduction of MDA level in jelantah exposed rats.
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