Media Jurnal Penelitian Medika Eksakta Quantity : 7 - No. 1 - 2008-04-10 Author : Nur Chámidah
lNFERENSI KURVA REGRESI N0NPARAMETRIK BERDASARKAN ESTIMATOR P0LINOMIAL LOKAL DENGAN ERROR LOGNORMAL Abstrak : Many of statistical analysis data in regression models use normal error supposition, but not really all real phenomenon reaches the normality supposition. In the nearly all real situations we frequently discover lognormal trend, for examples, call length for each specific of telephone user (Bolotin,1994); response time centered on numerical psychology sights (Breukelen, 1995); non compartmental pharmacokinetic adjustable in some scientific trials (Lacey et aI, 1997). Eckhard et al., (2001) demonstrated that lognormality trend can be discovered on the hereditary physic industry; on the seed psychology industry, on the foods technology industry for instance food control with dispersion process and filtering. Chamidah (2004) offers carried out a research of confidence interval estimation of nonparametric regression contour with lognormal mistake centered on Spline Estimator, Local Polynomial Estimator ánd Kernel Estimator. Thé goals of this analysis are to understand the substantial curve of the nonparametric regression estimation centered on nearby polynomial estimator, and made applications on Software S-Plus 2000 used to Gmelina Arborea Roxb Forest data in HTI-Tráns Wanakasita Nusantara Jámbi region. Research results has been an estimated model: with level of significant ï.¡=5%, that not really all regression coefficients had been similar to zero. Consequently, the model was substantial with determination coefficient (Ur2) 0,9961605. The specific testing of substantial its regression coefficient, we.elizabeth., ï.¢0, ï.¢1 and ï.¢2, with degree of significant ï.¡=5%, and came to the conclusion that all regression coefficient are usually significant with the model. On the some other hand, that regional polynomial éstimator in nonparametric régression model with lognormal error is appropriate to calculate volume of Gmelina Arborea Robx bottom on shrub size.
Keyword : Nonparametric regression, regional polinomial estimator, lognormal error
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lNFERENSI KURVA REGRESI N0NPARAMETRIK BERDASARKAN ESTIMATOR P0LINOMIAL LOKAL DENGAN ERROR LOGNORMAL Abstrak : Many of statistical analysis data in regression models use normal error supposition, but not really all real phenomenon reaches the normality supposition. In the nearly all real situations we frequently discover lognormal trend, for examples, call length for each specific of telephone user (Bolotin,1994); response time centered on numerical psychology sights (Breukelen, 1995); non compartmental pharmacokinetic adjustable in some scientific trials (Lacey et aI, 1997). Eckhard et al., (2001) demonstrated that lognormality trend can be discovered on the hereditary physic industry; on the seed psychology industry, on the foods technology industry for instance food control with dispersion process and filtering. Chamidah (2004) offers carried out a research of confidence interval estimation of nonparametric regression contour with lognormal mistake centered on Spline Estimator, Local Polynomial Estimator ánd Kernel Estimator. Thé goals of this analysis are to understand the substantial curve of the nonparametric regression estimation centered on nearby polynomial estimator, and made applications on Software S-Plus 2000 used to Gmelina Arborea Roxb Forest data in HTI-Tráns Wanakasita Nusantara Jámbi region. Research results has been an estimated model: with level of significant ï.¡=5%, that not really all regression coefficients had been similar to zero. Consequently, the model was substantial with determination coefficient (Ur2) 0,9961605. The specific testing of substantial its regression coefficient, we.elizabeth., ï.¢0, ï.¢1 and ï.¢2, with degree of significant ï.¡=5%, and came to the conclusion that all regression coefficient are usually significant with the model. On the some other hand, that regional polynomial éstimator in nonparametric régression model with lognormal error is appropriate to calculate volume of Gmelina Arborea Robx bottom on shrub size.
Keyword : Nonparametric regression, regional polinomial estimator, lognormal error
Page 1
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Inferensi Statistik Inferensi statistik adalah pengambilan kesimpulan tentang parameter populasi berdasarkan analisa pada sampel. Beberapa hal yang perlu diketahui berhubungan dengan inferensi statistik yaitu estimasi titik, estimasi interval dan uji hipotesis. Estimasi titik adalah menduga nilai tunggal parameter populasi. Estimasi Interval. Uji Hipotesis dan Estimasi interval Hubungan Antara Uji Hipotesis dan Estimasi interval Jika diperhatikan, terdapat kesamaan rumus-rumus yang dipakai pada saat pengujian hipotesis dan pendugaan selang kepercayaan. Untuk memperjelas kaitannya akan dijelaskan dengan menggunakan inferensi mean suatu populasi normal.