OUTPUT DENGAN PROGRAM MINITAB

Uji untuk Mendapatkan Parameter , Uji Serentak, Uji Parsial, dan Uji Durbin Watson

Regression Analysis: y versus x1, x2 The regression equation isy = 1.07 + 0.0641 x1 + 0.173 x2Predictor Coef SE Coef T P VIFConstant 1.0731 0.1550 6.92 0.000x1 0.06410 0.01673 3.83 0.006 1.8x2 0.17308 0.06343 2.73 0.029 1.8S = 0.0738774 R-Sq = 90.1% R-Sq(adj) = 87.2%Analysis of VarianceSource DF SS MS F PRegression 2 0.34679 0.17340 31.77 0.000Residual Error 7 0.03821 0.00546Total 9 0.38500Source DF Seq SSx1 1 0.30616x2 1 0.04064Unusual ObservationsObs x1 y Fit SE Fit Residual St Resid 1 9.0 1.8000 1.6500 0.0369 0.1500 2.34RR denotes an observation with a large standardized residual.Durbin-Watson statistic = 1.32124

Uji Kolmogorov Smirnov

Uji Asumsi Varians Residual Identik dengan Uji Glesjer

Regression Analysis: yi versus x1, x2 The regression equation isyi = 0.0868 - 0.00131 x1 - 0.0444 x2Predictor Coef SE Coef T P VIFConstant 0.08683 0.08388 1.04 0.335x1 -0.001315 0.009051 -0.15 0.889 1.8x2 -0.04436 0.03432 -1.29 0.237 1.8S = 0.0399699 R-Sq = 32.9% R-Sq(adj) = 13.7%Analysis of VarianceSource DF SS MS F PRegression 2 0.005483 0.002741 1.72 0.247Residual Error 7 0.011183 0.001598Total 9 0.016666Source DF Seq SSx1 1 0.002813x2 1 0.002670Unusual ObservationsObs x1 yi Fit SE Fit Residual St Resid 1 9.0 0.1500 0.0750 0.0200 0.0750 2.17RR denotes an observation with a large standardized residual.Durbin-Watson statistic = 2.65750

Uji Kolinearitas untuk Melihat Ada Tidaknya Korelasi X1 dan X2

Regression Analysis: xi versus xii The regression equation isxi = 9.00 + 2.50 xiiPredictor Coef SE Coef T PConstant 9.0000 0.7806 11.53 0.000xii 2.500 1.008 2.48 0.038S = 1.56125 R-Sq = 43.5% R-Sq(adj) = 36.4%Analysis of VarianceSource DF SS MS F PRegression 1 15.000 15.000 6.15 0.038Residual Error 8 19.500 2.437Total 9 34.500Durbin-Watson statistic = 1.06410