Deskripsi Rasio Stok terhadap Produksi Rasio Stok Terhadap Produksi Kabupaten Subang

Psstokt 0.0006 0.9998 0.0193 0.3899 Pskonsum 0.00000 0.81107 0.01450 0.10300 Pssosial 0.00000 0.49843 0.01698 0.11999 Pbepak 0.00000 0.16471 0.00000 0.00535 Descriptive Statistics: Psprod, Psberi, ... by Wilayah Variable Wilayah N Mean Median TrMean StDev Psprod 1 127 0.8862 0.9046 0.8969 0.1198 2 90 0.96002 0.98119 0.96632 0.04812 Psberi 1 127 0.02469 0.00000 0.00951 0.08271 2 90 0.02189 0.00000 0.01474 0.04193 Psstok 1 127 0.09123 0.07661 0.08355 0.08670 2 90 0.01810 0.00741 0.01230 0.03316 Psjual 1 127 0.3667 0.3672 0.3577 0.3022 2 90 0.8649 0.9039 0.8749 0.1098 Psguna 1 127 0.2431 0.1895 0.2262 0.2164 2 90 0. 09330 0.06345 0.08260 0.08753 Psstokt 1 127 0.3904 0.3174 0.3795 0.3096 2 90 0.04177 0.01973 0.03274 0.05738 Pskonsum 1 127 0.1221 0.0646 0.1014 0.1551 2 90 0.03816 0.02309 0.03253 0.03954 Pssosial 1 127 0.11280 0.07978 0.10334 0.10986 2 90 0.05036 0.03175 0.04386 0.05250 Pbepak 1 127 0.00823 0.00000 0.00650 0.01361 2 90 0.00478 0.00000 0.00153 0.02102 Variable Wilayah SE Mean Minimum Maximum Q1 Q3 Psprod 1 0.0106 0.3873 1.1561 0.8492 0.9745 2 0.00507 0.77253 1.00000 0.93925 0.99086 Psberi 1 0.00734 0.00000 0.56301 0.00000 0.00000 2 0.00442 0.00000 0.19257 0.00000 0.02522 Psstok 1 0.00769 0.00000 0.37532 0.01442 0.14116 2 0.00350 0.00000 0.22747 0.00206 0.01654 Psjual 1 0.0268 0.0000 0.9785 0.0000 0.6284 2 0.0116 0.4601 0.9890 0.8047 0.9500 Psguna 1 0.0192 0.0000 0.9709 0.0730 0.3508 2 0.00923 0.00411 0.50033 0.03354 0.12718 Psstokt 1 0.0275 0.0061 0.9998 0.1045 0.6250 2 0.00605 0.00061 0.28606 0.00603 0.05546 Pskonsum 1 0.0138 0.0000 0.8111 0.0178 0.1697 2 0.00417 0.00080 0.21067 0.01199 0.04960 Pssosial 1 0.00975 0.00000 0.49843 0.01905 0.16808 2 0.00553 0.00000 0.28654 0.01547 0.06725 Pbepak 1 0.00121 0.00000 0.06675 0.00000 0.01322 2 0.00222 0.00000 0.16471 0.00000 0.00323

3. Deskripsi Rasio Stok terhadap Produksi Rasio Stok Terhadap Produksi Kabupaten Subang

Descriptive Statistics: Rt-1; Rt-2; Rt-3; Rt-4; Rt-5; Rt-6; Rt-7 Variable N N Mean Median TrMean StDev Rt-1 399 35 0,3415 0,2597 0,3229 0,2968 Rt-2 359 75 0,2349 0,1628 0,2181 0,2123 Rt-3 331 103 0,16022 0,10697 0,14708 0,15045 Rt-4 260 174 0,10691 0,06280 0,09396 0,11816 Rt-5 143 291 0,07596 0,04348 0,06737 0,08143 Rt-6 91 343 0,04755 0,02025 0,03915 0,06125 Rt-7 51 383 0,02695 0,00925 0,02118 0,03801 Variable SE Mean Minimum Maximum Q1 Q3 Rt-1 0,0149 0,0025 1,1838 0,0908 0,5196 Rt-2 0,0112 0,0007 0,9570 0,0648 0,3616 Rt-3 0,00827 0,00061 0,68788 0,04042 0,25765 Rt-4 0,00733 0,00000 0,71941 0,02206 0,16511 Rt-5 0,00681 0,00200 0,47979 0,01503 0,12417 Rt-6 0,00642 0,00000 0,37575 0,00870 0,07503 Rt-7 0,00532 0,00000 0,18426 0,00366 0,03042 Rasio Stok Terhadap Produksi Wilayah Non Pantura Descriptive Statistics: Rt-1; Rt-2; Rt-3; Rt-4; Rt-5; Rt-6; Rt-7 Variable N N Mean Median TrMean StDev Rt-1 203 33 0,5037 0,4667 0,5004 0,2897 Rt-2 171 65 0,3564 0,3348 0,3494 0,2150 Rt-3 149 87 0,2432 0,2150 0,2366 0,1604 Rt-4 114 122 0,1538 0,1147 0,1412 0,1374 Rt-5 42 193 0,1349 0,1103 0,1262 0,1036 Rt-6 15 220 0,1189 0,0961 0,1080 0,0979 Rt-7 5 178 0,0936 0,0951 0,0936 0,0613 Variable SE Mean Minimum Maximum Q1 Q3 Rt-1 0,0203 0,0063 1,1838 0,2700 0,7627 Rt-2 0,0164 0,0063 0,8921 0,1688 0,5217 Rt-3 0,0131 0,0072 0,6879 0,1120 0,3437 Rt-4 0,0129 0,0000 0,7194 0,0419 0,2400 Rt-5 0,0160 0,0106 0,4798 0,0469 0,1823 Rt-6 0,0253 0,0032 0,3758 0,0476 0,1804 Rt-7 0,0274 0,0209 0,1843 0,0394 0,1469 Rasio Stok Tehadap Produksi Wilayah Pantura Descriptive Statistics: Rt-1; Rt-2; Rt-3; Rt-4; Rt-5; Rt-6; Rt-7 Variable N N Mean Median TrMean StDev Rt-1 196 2 0,1735 0,1043 0,1479 0,1927 Rt-2 188 10 0,12443 0,07495 0,10893 0,13584 Rt-3 182 16 0,09232 0,05095 0,08127 0,09964 Rt-4 146 51 0,07032 0,03329 0,06019 0,08458 Rt-5 101 89 0,05146 0,02800 0,04616 0,05436 Rt-6 76 113 0,03347 0,01680 0,02913 0,03863 Rt-7 46 143 0,01971 0,00849 0,01673 0,02692 Variable SE Mean Minimum Maximum Q1 Q3 Rt-1 0,0138 0,0025 0,9914 0,0485 0,2223 Rt-2 0,00991 0,00073 0,95697 0,03355 0,16325 Rt-3 0,00739 0,00061 0,56978 0,02533 0,12429 Rt-4 0,00700 0,00073 0,52544 0,01687 0,08955 Rt-5 0,00541 0,00200 0,23323 0,01359 0,06729 Rt-6 0,00443 0,00000 0,17987 0,00771 0,04320 Rt-7 0,00397 0,00000 0,12651 0,00292 0,02361 90 Lampiran 2. Pendugaan Stok di Rumah Tangga Petani Berdasarkan Hasil Survei No. Kecamatan Dugaan Dugaan Stok Jul Jun Mei Apr Mar Peb Jan Juli Juni Mei April Maret Feb Jan Stok Gabah Setara Beras Ton 1 Sagalaherang 0,5182 - 0,2714 0,2740 0,2397 - 0,1296 3499 - 1072 7375 6569 - 490 5762,901 3642,153 2 Jalancagak - - - 0,1536 0,1098 - 0,0523 - - - 4033 5162 - 996 1238,328 782,623 3 Cisalak 0,3524 0,3210 0,2122 0,1301 0,1681 0,1654 - 6052 308,55 3777 2981 2812 6528 - 4973,505 3143,255 4 Tanjungsiang 0,7257 - 0,3146 0,1905 0,2023 0,1517 - 191 - 7826 3237 2440 256 - 3749,770 2369,855 5 Cijambe 0,5565 0,3487 0,2266 0,1335 0,1277 0,0664 0,0209 2950 31,9 801 4837 1510 1034 121 2744,075 1734,256 6 Cibogo 0,5859 - 0,3169 0,1350 0,1096 - - 409 - 597 4371 4112 - - 1469,583 928,776 7 Cipunagara 0,2475 - - 0,0572 0,0360 - - 9622 - - 15617 1410 - - 3325,548 2101,746 8 Pagaden 0,3866 - - 0,1270 0,0876 0,0605 0,0417 5534 - - 16967 1034 1436 968 4512,092 2851,642 9 Cipeundeuy 0,7126 - 0,3790 0,2562 0,1550 - - 2510 - 301 5556 2159 - - 3660,797 2313,624 10 Purwadadi - - - 0,1790 0,1096 - - - - - 5091 3031 - - 1243,487 785,884 11 Ciasem 0,1192 - 0,0681 0,0471 0,0437 0,0294 - 1365 - 2363 9137 2709 1924 - 928,853 587,035 12 Binong - - 0,0811 0,0616 0,0428 0,0334 - - - 8095 28240 2447 360 - 2512,826 1588,106 13 Compreng - - 0,0761 0,0639 0,0646 - - - - 4023 27064 8234 - - 2567,456 1622,632 14 Pusakanagara 0,2531 0,2075 0,1620 0,1382 - 0,0519 0,0370 1206 292,62 26248 1682 - 149,8 2065 4934,780 3118,781 15 Pamanukan - 0,0448 0,0364 0,0307 - - 0,0044 - 204 11587 14889 - - 1182 893,210 564,509 16 Blanakan - - 0,0409 0,0344 0,0131 - - - - 9202 17721 4200 - - 1040,942 657,875 17 Legonkulon 0,1718 0,1363 0,1157 - - - - 2124 249,72 15239 - - - - 2162,092 1366,442 18 Cikaum 0,5374 - - - 0,1005 - - 5219 - - - 1672 - - 2972,646 1878,712 Rasio Stok Agustus terhadap panen bulan : Produksi 2005 Ton Lampiran 3 Model Stok Petani Best Subsets Regression: Sagt versus Pjul, Pjun, Pmei, Papr, Pmar, D Response is Sagt P P P P P j j m a m u u e p a Vars R-Sq R-Sqadj C-p S l n i r r D 1 28.3 23.8 47.8 849.76 X 1 9.7 4.0 63.9 953.82 X 2 35.2 26.6 43.8 834.26 X X 2 33.6 24.8 45.2 844.44 X X 3 58.9 50.1 25.4 687.58 X X X 3 45.3 33.6 37.1 793.26 X X X 4 76.6 69.4 12.1 538.14 X X X X 4 74.7 66.9 13.8 560.31 X X X X 5 86.3 80.6 5.8 428.29 X X X X X 5 79.0 70.2 12.1 531.15 X X X X X 6 87.2 80.3 7.0 432.36 X X X X X X Regression Analysis: Sagt versus Pjul, Pmei, Papr, Pmar, D The regression equation is Sagt = 40 + 0.334 Pjul + 0.177 Pmei + 0.0506 Papr + 0.194 Pmar - 2103 D Predictor Coef SE Coef T P VIF Constant 39.6 324.6 0.12 0.905 Pjul 0.33440 0.04592 7.28 0.000 1.5 Pmei 0.17718 0.02616 6.77 0.000 3.1 Papr 0.05062 0.01734 2.92 0.013 2.1 Pmar 0.19397 0.06058 3.20 0.008 1.7 D -2102.9 361.6 -5.82 0.000 3.2 S = 428.3 R-Sq = 86.3 R-Sqadj = 80.6 PRESS = 4372515 R-Sqpred = 72.87 Analysis of Variance Source DF SS MS F P Regression 5 13913672 2782734 15.17 0.000 Residual Error 12 2201143 183429 Total 17 16114815 Source DF Seq SS Pjul 1 4561216 Pmei 1 1113759 Papr 1 56319 Pmar 1 1978923 D 1 6203454 Obs Pjul Sagt Fit SE Fit Residual St Resid 1 3499 3642 3047 263 595 1.76 2 0 783 1245 203 -462 -1.23 3 6052 3143 3429 226 -286 -0.79 4 191 2370 2127 193 243 0.63 5 2950 1734 1706 165 29 0.07 6 409 929 1301 174 -372 -0.95 7 9622 2102 2218 350 -116 -0.47 8 5534 2852 2950 295 -98 -0.32 9 2510 2314 1632 155 681 1.71 10 0 786 885 199 -99 -0.26 11 1365 587 53 274 534 1.62 12 0 1588 1275 284 313 0.98 13 0 1623 1616 323 6 0.02 14 1206 3119 3076 356 43 0.18 15 0 565 743 236 -179 -0.50 16 0 658 1279 181 -621 -1.60 17 2124 1366 1347 247 20 0.06 18 5219 1879 2109 189 -230 -0.60 Predicted Values for New Observations New Obs Fit SE Fit 95.0 CI 95.0 PI 1 3047 263 2475, 3619 1952, 4142 2 1245 203 803, 1687 212, 2278 3 3429 226 2936, 3922 2373, 4484 4 2127 193 1708, 2547 1104, 3150 5 1706 165 1347, 2064 706, 2705 6 1301 174 921, 1681 294, 2308 7 2218 350 1457, 2980 1014, 3423 8 2950 295 2306, 3593 1816, 4083 9 1632 155 1296, 1969 640, 2624 10 885 199 452, 1318 -143, 1914 11 53 274 -545, 651 -1055, 1161 12 1275 284 657, 1893 156, 2394 13 1616 323 913, 2320 448, 2785 14 3076 356 2301, 3850 1863, 4289 15 743 236 229, 1257 -322, 1809 16 1279 181 884, 1673 266, 2292 17 1347 247 808, 1886 269, 2425 18 2109 189 1697, 2522 1089, 3129 19 3244 219 2767, 3720 2196, 4291 20 3828 310 3154, 4503 2677, 4980 21 1590 375 773, 2407 350, 2830 22 4560 740 2946, 6173 2696, 6423 XX X denotes a row with X values away from the center XX denotes a row with very extreme X values Values of Predictors for New Observations New Obs Pjul Pmei Papr Pmar D 1 3499 1072 7375 6569 0.00 2 0 0 4033 5162 0.00 3 6052 3777 2981 2812 0.00 4 191 7826 3237 2440 0.00 5 2950 801 4837 1510 0.00 6 409 597 4371 4112 0.00 7 9622 0 15617 1410 1.00 8 5534 0 16967 1034 0.00 9 2510 301 5556 2159 0.00 10 0 0 5091 3031 0.00 11 1365 4363 7137 2709 1.00 12 0 8095 28240 2447 1.00 13 0 4023 27064 8234 1.00 14 1206 26248 1682 0 1.00 15 0 11587 14889 0 1.00 16 0 9202 17721 4200 1.00 17 2124 15239 0 0 1.00 18 5219 0 0 1672 0.00 19 5164 3562 9379 1914 0.00 20 6177 656 8926 5956 0.00 21 8928 0 7200 1563 1.00 22 18511 0 0 2232 1.00 Normplot of Residuals for Sagt 500 -500 2 1 -1 -2 N o rm a l S c o re Residual Normal Probability Plot of the Residuals response is Sagt Approximate P-Value 0.15 D+: 0.174 D-: 0.098 D : 0.174 Kolmogorov-Smirnov Normality Test N: 18 StDev: 359.832 Average: -0.0000000 500 -500 .999 .99 .95 .80 .50 .20 .05 .01 .001 P robab ili ty RESI3 Uji Kenormlan Residuals vs Fits for Sagt 3500 3000 2500 2000 1500 1000 500 500 -500 Fitted Value R e s idua l Residuals Versus the Fitted Values response is Sagt Correlations: Pjul, Pjun, Pmei, Papr, Pmar Pjul Pjun Pmei Papr Pjun 0.073 0.775 Pmei -0.330 0.694 0.181 0.001 Papr -0.113 -0.324 -0.060 0.654 0.190 0.814 Pmar -0.267 -0.479 -0.418 0.316 0.284 0.044 0.084 0.202 Cell Contents: Pearson correlation P-Value Lampiran 4 Pendugaan Stok Beras di Penggilingan Berdasarkan Hasil Survei Kode Kecamatan Jumlah Jumlah Rataan Standar Dugaan Kec Contoh Populasi Contoh Deviasi Stok Kecamatan Kuintal Beras 1 Sagalaherang 3 11 26,5 32,6 291,5 2 Jalancagak 3 6 62,1 72,1 372,6 3 Cisalak 4 43 67,1 38,6 2885,3 4 Tanjungsiang 3 10 59,9 32,8 599,0 5 Cijambe 4 37 103,4 67,4 3825,8 6 Cibogo 3 9 147,7 105,7 1329,3 7 Cipunagara 3 62 185,2 126 11482,4 8 Pagaden 5 14 330 407 4620,0 9 Cipeundeuy 5 9 65,5 36,2 589,5 10 Purwadadi 3 3 340 218 1020,0 11 Ciasem 5 19 201,2 173,7 3822,8 12 Binong 5 47 278,33 404 13081,5 13 Compreng 3 16 130 48 2080,0 14 Pusakanagara 5 24 166,8 135,5 4003,2 15 Pamanukan 6 21 180,6 174,9 3792,6 16 Blanakan 5 15 320,1 202,3 4801,5 18 Cikaum 5 8 57,8 87,9 462,4 95 Ragam Dugaan Kecamatan Batas Atas Batas bawah 31174,3 -54,56 637,56 31190,5 26,45 718,75 624665,7 1336,20 4434,40 25102,9 288,46 909,54 1386677,5 1517,76 6133,84 201104,8 450,34 2208,26 19358136,0 2858,82 20105,98 4174354,8 615,48 8624,52 9435,2 399,12 779,88 0,0 1020,00 1020,00 1605133,9 1339,60 6306,00 64437676,8 -2652,01 28815,03 159744,0 1296,63 2863,37 1674454,8 1466,95 6539,45 1605975,5 1308,75 6276,45 1227758,7 2629,74 6973,26 37086,8 84,94 839,86 Selang Kepercayaan 95 Lampiran 5 Model Stok Penggilingan Best Subsets Regression: STagt versus Pjul, Pjun, ... Response is STagt P P P P P P P K K j j m a m j m u u e p a u a Vars R-Sq R-Sqadj C-p S l n i r r l r 1 45.3 41.7 30.1 264.44 X 1 22.3 17.1 48.3 315.20 X 2 70.3 66.1 12.4 201.57 X X 2 67.4 62.8 14.7 211.21 X X 3 81.7 77.4 5.5 164.48 X X X 3 79.3 74.5 7.3 174.78 X X X 4 85.3 80.4 4.6 153.24 X X X X 4 82.8 77.0 6.6 165.97 X X X X 5 88.5 83.3 4.0 141.32 X X X X X 5 86.0 79.7 6.0 156.12 X X X X X 6 88.6 81.7 6.0 147.93 X X X X X X 6 88.5 81.7 6.0 148.18 X X X X X X 7 88.6 79.7 8.0 155.93 X X X X X X X Regression Analysis: STagt versus Pjul, Pjun, Pmei, Papr, Pmar The regression equation is STagt = 22 + 0.0521 Pjul - 0.427 Pjun + 0.0140 Pmei + 0.0316 Papr - 0.0547 Pmar Predictor Coef SE Coef T P VIF Constant 21.5 144.7 0.15 0.884 Pjul 0.05214 0.02213 2.36 0.038 1.6 Pjun -0.4271 0.7034 -0.61 0.556 2.4 Pmei 0.01397 0.01209 1.16 0.272 2.9 Papr 0.031627 0.006162 5.13 0.000 1.2 Pmar -0.05465 0.02646 -2.07 0.063 1.4 S = 193.7 R-Sq = 78.5 R-Sqadj = 68.7 PRESS = 1343376 R-Sqpred = 29.94 Analysis of Variance Source DF SS MS F P Regression 5 1504803 300961 8.02 0.002 Residual Error 11 412762 37524 Total 16 1917565 Source DF Seq SS Pjul 1 123195 Pjun 1 55 Pmei 1 355709 Papr 1 865721 Pmar 1 160123 Obs Pjul STagt Fit SE Fit Residual St Resid 1 3499 29.2 93.2 111.8 -64.1 -0.40 2 2505 37.3 -3.4 84.6 40.6 0.23 3 6052 288.5 198.7 170.6 89.9 0.98 4 191 59.9 109.8 97.2 -49.9 -0.30 5 2950 382.6 243.4 72.8 139.2 0.78 6 409 132.9 -35.3 83.1 168.2 0.96 7 9622 1148.2 940.1 152.8 208.2 1.75 8 5534 462.0 790.2 102.2 -328.2 -1.99 9 2510 59.0 214.3 69.4 -155.4 -0.86 10 0 102.0 16.9 93.4 85.1 0.50 11 1365 382.3 308.4 74.0 73.9 0.41 12 0 1169.0 894.0 123.1 275.0 1.84 13 0 325.0 483.7 149.1 -158.7 -1.28 14 1206 400.3 379.3 175.1 21.0 0.25 15 0 379.3 567.2 129.7 -187.9 -1.31 16 0 480.2 481.0 89.2 -0.9 -0.00 17 5219 46.2 202.3 95.5 -156.0 -0.93 Regression Analysis: STagt versus Pjul, Pmei, Papr, Pmar The regression equation is STagt = 22 + 0.0472 Pjul + 0.00886 Pmei + 0.0328 Papr - 0.0543 Pmar Predictor Coef SE Coef T P VIF Constant 21.6 140.8 0.15 0.881 Pjul 0.04716 0.02001 2.36 0.036 1.4 Pmei 0.008861 0.008453 1.05 0.315 1.5 Papr 0.032813 0.005688 5.77 0.000 1.1 Pmar -0.05428 0.02574 -2.11 0.057 1.4 S = 188.5 R-Sq = 77.8 R-Sqadj = 70.3 PRESS = 1168687 R-Sqpred = 39.05 Analysis of Variance Source DF SS MS F P Regression 4 1490967 372742 10.49 0.001 Residual Error 12 426598 35550 Total 16 1917565 Source DF Seq SS Pjul 1 123195 Pmei 1 162411 Papr 1 1047331 Pmar 1 158030 Obs Pjul STagt Fit SE Fit Residual St Resid 1 3499 29.2 81.6 107.2 -52.4 -0.34 2 2505 37.3 -9.1 81.8 46.4 0.27 3 6052 288.5 285.7 90.0 2.9 0.02 4 191 59.9 73.7 74.8 -13.8 -0.08 5 2950 382.6 244.6 70.8 138.0 0.79 6 409 132.9 -33.6 80.8 166.5 0.98 7 9622 1148.2 911.3 141.4 236.9 1.90 8 5534 462.0 783.2 98.9 -321.2 -2.00R 9 2510 59.0 207.8 66.7 -148.8 -0.84 10 0 102.0 24.1 90.2 77.9 0.47 11 1365 382.3 316.3 70.9 66.0 0.38 12 0 1169.0 887.2 119.3 281.8 1.93 13 0 325.0 498.4 143.2 -173.4 -1.41 14 1206 400.3 366.2 169.1 34.1 0.41 15 0 379.3 612.8 102.9 -233.6 -1.48 16 0 480.2 456.6 77.5 23.5 0.14 17 5219 46.2 177.0 83.6 -130.7 -0.77 R denotes an observation with a large standardized residual Regression Analysis: STagt versus Pjul, Pmei, Papr, Pmar, PKjul, PKmar The regression equation is STagt = - 222 - 0.0031 Pjul + 0.0180 Pmei + 0.0324 Papr + 0.114 Pmar +0.000007 PKjul -0.000020 PKmar Predictor Coef SE Coef T P VIF Constant -221.6 192.2 -1.15 0.276 Pjul -0.00311 0.04517 -0.07 0.947 11.6 Pmei 0.018006 0.008056 2.24 0.049 2.2 Papr 0.032416 0.005639 5.75 0.000 1.7 Pmar 0.11351 0.07021 1.62 0.137 17.3 PKjul 0.00000746 0.00000502 1.49 0.168 10.4 PKmar -0.00001960 0.00000815 -2.41 0.037 15.7 S = 148.2 R-Sq = 88.5 R-Sqadj = 81.7 PRESS = 1333831 R-Sqpred = 30.44 Analysis of Variance Source DF SS MS F P Regression 6 1697992 282999 12.89 0.000 Residual Error 10 219573 21957 Total 16 1917565 Source DF Seq SS Pjul 1 123195 Pmei 1 162411 Papr 1 1047331 Pmar 1 158030 PKjul 1 79976 PKmar 1 127049 Obs Pjul STagt Fit SE Fit Residual St Resid 1 3499 29.2 17.2 88.1 12.0 0.10 2 2505 37.3 -35.9 77.9 73.2 0.58 3 6052 288.5 361.8 82.0 -73.3 -0.59 4 191 59.9 184.2 73.2 -124.3 -0.97 5 2950 382.6 132.1 67.1 250.5 1.90 6 409 132.9 66.2 76.6 66.7 0.53 7 9622 1148.2 1066.8 139.7 81.5 1.65 8 5534 462.0 636.2 99.4 -174.2 -1.58 9 2510 59.0 156.9 57.2 -97.9 -0.72 10 0 102.0 107.4 88.7 -5.4 -0.05 11 1365 382.3 324.5 58.6 57.7 0.42 12 0 1169.0 1000.0 114.7 169.0 1.80 13 0 325.0 334.0 134.1 -9.0 -0.14 14 1206 400.3 312.7 134.1 87.7 1.39 15 0 379.3 469.7 103.4 -90.4 -0.85 16 0 480.2 649.6 93.2 -169.4 -1.47 17 5219 46.2 100.5 70.3 -54.2 -0.42 Model Terbaik adalah : Regression Analysis: STagt versus Pjul, Pmei, Papr, Pmar, PKmar The regression equation is STagt = - 341 + 0.0595 Pjul + 0.0203 Pmei + 0.0370 Papr + 0.125 Pmar -0.000022 PKmar Predictor Coef SE Coef T P VIF Constant -341.1 183.9 -1.85 0.091 Pjul 0.05947 0.01726 3.45 0.005 1.5 Pmei 0.020348 0.008323 2.44 0.033 2.1 Papr 0.036978 0.004985 7.42 0.000 1.2 Pmar 0.12513 0.07351 1.70 0.117 17.1 PKmar -0.00002159 0.00000847 -2.55 0.027 15.3 S = 156.1 R-Sq = 86.0 R-Sqadj = 79.7 PRESS = 859431 R-Sqpred = 55.18 Analysis of Variance Source DF SS MS F P Regression 5 1649468 329894 13.54 0.000 Residual Error 11 268097 24372 Total 16 1917565 Source DF Seq SS Pjul 1 123195 Pmei 1 162411 Papr 1 1047331 Pmar 1 158030 PKmar 1 158501 Obs Pjul STagt Fit SE Fit Residual St Resid 1 3499 29.2 51.8 89.5 -22.7 -0.18 2 2505 37.3 26.5 69.2 10.7 0.08 3 6052 288.5 387.1 84.5 -98.6 -0.75 4 191 59.9 126.0 65.2 -66.1 -0.47 5 2950 382.6 169.3 65.7 213.3 1.51 6 409 132.9 6.5 68.7 126.4 0.90 7 9622 1148.2 942.2 117.7 206.1 2.01R 8 5534 462.0 721.8 85.3 -259.8 -1.99 9 2510 59.0 189.3 55.7 -130.4 -0.89 10 0 102.0 28.1 74.7 73.9 0.54 11 1365 382.3 345.9 59.8 36.4 0.25 12 0 1169.0 1044.8 116.5 124.2 1.20 13 0 325.0 308.0 140.1 17.0 0.25 14 1206 400.3 327.0 140.9 73.4 1.09 15 0 379.3 445.3 107.6 -66.0 -0.58 16 0 480.2 646.1 98.2 -166.0 -1.37 17 5219 46.2 118.2 73.0 -72.0 -0.52 R denotes an observation with a large standardized residual Normplot of Residuals for STagt 200 100 -100 -200 -300 2 1 -1 -2 N o rm al S c o re Residual Normal Probability Plot of the Residuals response is STagt Approximate P-Value 0.15 D+: 0.107 D-: 0.067 D : 0.107 Kolmogorov-Smirnov Normality Test N: 17 StDev: 129.445 Average: -0.0000000 200 100 -100 -200 .999 .99 .95 .80 .50 .20 .05 .01 .001 P robab il it y RESI1 Uji Kenormalan Sisaan Residuals vs Fits for STagt 1000 500 200 100 -100 -200 -300 Fitted Value R e si dua l Residuals Versus the Fitted Values response is STagt Predicted Values for New Observations New Obs Fit SE Fit 95.0 CI 95.0 PI 1 51.8 89.5 -145.3, 248.9 -344.3, 447.9 2 26.5 69.2 -125.7, 178.8 -349.3, 402.3 3 387.1 84.5 201.1, 573.1 -3.6, 777.8 4 126.0 65.2 -17.5, 269.5 -246.4, 498.4 5 169.3 65.7 24.8, 313.8 -203.5, 542.0 6 6.5 68.7 -144.8, 157.7 -368.9, 381.9 7 942.2 117.7 683.1, 1201.3 511.9, 1372.5 8 721.8 85.3 534.0, 909.6 330.2, 1113.4 9 189.3 55.7 66.7, 311.9 -175.5, 554.1 10 28.1 74.7 -136.3, 192.5 -352.8, 409.0 11 345.9 59.8 214.2, 477.6 -22.1, 713.9 12 1044.8 116.5 788.3, 1301.3 616.0, 1473.6 13 308.0 140.1 -0.4, 616.4 -153.8, 769.7 14 327.0 140.9 16.9, 637.0 -135.9, 789.8 15 445.3 107.6 208.5, 682.1 28.0, 862.6 16 646.1 98.2 430.0, 862.3 240.2, 1052.1 17 118.2 73.0 -42.5, 278.9 -261.1, 497.5 18 95.4 109.5 -145.7, 336.4 -324.4, 515.1 19 545.8 58.2 417.7, 673.8 179.1, 912.5 20 349.0 104.1 119.8, 578.2 -64.0, 762.1 21 599.0 99.1 380.8, 817.2 192.0, 1006.0 22 931.6 259.1 361.4, 1501.8 265.8, 1597.3 XX X denotes a row with X values away from the center XX denotes a row with very extreme X values Values of Predictors for New Observations New Obs Pjul Pmei Papr Pmar PKmar 1 3499 1072 7375 6569 43151761 2 2505 0 4003 5162 26646244 3 6052 3777 2981 2812 7907344 4 191 7826 3237 2440 5953600 5 2950 801 4837 1510 2280100 6 409 597 4371 4112 16908544 7 9622 0 15617 1410 1988100 8 5534 0 16967 1034 1069156 9 2510 301 5556 2159 4661281 10 0 0 5091 3031 9186961 11 1365 0 11500 2709 7338681 12 0 8095 28240 2447 5987809 13 0 4023 27064 8234 67798756 14 1206 26248 1682 0 0 15 0 11587 14889 0 0 16 0 9202 17721 4200 17640000 17 5219 0 0 1672 2795584 18 2124 15239 0 0 0 19 5164 3562 9379 1914 3663396 20 6177 656 8926 5956 35473936 21 8928 0 7200 1563 2442969 22 18511 0 0 2232 4981824 Lampiran 6 Model Stok Sub Dolog Plot data asli Ja n -9 8 N o v- 98 S e p- 99 Ju l-0 M a y -0 1 M ar -0 2 Ja n -0 3 N o v- 0 3 S e p- 0 4 Ju l-0 5 10000000 20000000 30000000 DateTime S to k Plot Data setelah di Logaritma Ju l-0 5 S e p- 0 4 N o v- 0 3 Ja n -0 3 M ar -0 2 M a y -0 1 Ju l-0 S e p- 99 N o v- 98 Ja n -9 8 7.5 6.5 5.5 4.5 DateTime L st o k 40 30 20 10 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 A u to c o rr e la ti on LBQ T Corr Lag LBQ T Corr Lag LBQ T Corr Lag LBQ T Corr Lag 182.80 182.80 182.71 182.69 181.94 179.50 175.47 170.20 163.38 155.00 146.95 140.04 136.63 136.32 135.62 134.00 131.66 128.86 126.63 126.14 125.92 124.07 120.37 116.87 116.01 115.96 115.08 112.99 109.59 105.23 101.49 99.74 99.41 99.40 98.73 97.07 95.96 95.93 91.14 65.84 0.01 0.11 -0.05 -0.34 -0.61 -0.80 -0.93 -1.07 -1.22 -1.22 -1.15 -0.82 -0.25 0.38 0.58 0.71 0.78 0.71 0.33 -0.22 -0.66 -0.95 -0.93 -0.47 0.11 0.48 0.75 0.97 1.12 1.05 0.73 0.32 -0.05 -0.46 -0.73 -0.60 0.10 1.28 3.27 7.99 0.00 0.02 -0.01 -0.07 -0.12 -0.16 -0.18 -0.21 -0.24 -0.23 -0.22 -0.15 -0.05 0.07 0.11 0.13 0.14 0.13 0.06 -0.04 -0.12 -0.17 -0.17 -0.08 0.02 0.09 0.13 0.17 0.19 0.18 0.12 0.05 -0.01 -0.08 -0.12 -0.10 0.02 0.21 0.49 0.80 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Plot ACF 40 30 20 10 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 P a rt ia l A u to c o rr e la ti on T PAC Lag T PAC Lag T PAC Lag T PAC Lag -0.49 0.69 0.27 -0.26 0.74 0.70 -0.59 1.01 -1.70 -0.03 -0.01 -0.00 0.00 -1.46 0.22 0.82 -0.95 -0.32 -0.04 0.41 0.50 -0.45 -1.48 -0.99 -1.50 0.55 0.28 1.02 -1.68 0.27 0.73 0.80 0.28 0.81 2.15 0.13 -0.54 -1.17 -5.23 8.57 -0.05 0.07 0.03 -0.03 0.07 0.07 -0.06 0.10 -0.17 -0.00 -0.00 -0.00 0.00 -0.15 0.02 0.08 -0.09 -0.03 -0.00 0.04 0.05 -0.04 -0.15 -0.10 -0.15 0.05 0.03 0.10 -0.17 0.03 0.07 0.08 0.03 0.08 0.22 0.01 -0.05 -0.12 -0.52 0.86 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Plot PACF MODEL ARIMA 2,0,0 Final Estimates of Parameters Type Coef SE Coef T P AR 1 1.6762 0.1060 15.81 0.000 AR 2 -0.6764 0.1060 -6.38 0.000 Number of observations: 100 Residuals: SS = 8.08800 backforecasts excluded MS = 0.08253 DF = 98 Modified Box-Pierce Ljung-Box Chi-Square statistic Lag 12 24 36 48 Chi-Square 10.4 18.4 31.4 40.3 DF 10 22 34 46 P-Value 0.406 0.684 0.595 0.710 Forecasts from period 100 95 Percent Limits Period Forecast Lower Upper 101 7.1037 6.5405 7.6669 102 7.1677 6.0684 8.2669 103 7.2095 5.5811 8.8379 104 7.2364 5.1049 9.3678 105 7.2531 4.6510 9.8552 106 7.2630 4.2231 10.3029 Normplot of Residuals for Lstok 2 1 3 2 1 -1 -2 -3 N o rm a l S c o re Residual Normal Probability Plot of the Residuals response is Lstok Residuals vs Order for Lstok 100 90 80 70 60 50 40 30 20 10 2 1 Observation Order R e s idua l Residuals Versus the Order of the Data response is Lstok MODEL ARIMA 0,0,2 Final Estimates of Parameters Type Coef SE Coef T P MA 1 -1.7420 0.0272 -64.11 0.000 MA 2 -0.9608 0.0123 -77.80 0.000 Number of observations: 100 Residuals: SS = 393.001 backforecasts excluded MS = 4.010 DF = 98 Modified Box-Pierce Ljung-Box Chi-Square statistic Lag 12 24 36 48 Chi-Square 281.7 364.1 409.9 444.4 DF 10 22 34 46 P-Value 0.000 0.000 0.000 0.000 Forecasts from period 100 95 Percent Limits Period Forecast Lower Upper 101 5.6447 1.7189 9.5705 102 2.5542 -5.3311 10.4395 103 0.0000 -8.7410 8.7410 104 0.0000 -8.7410 8.7410 105 0.0000 -8.7410 8.7410 106 0.0000 -8.7410 8.7410 MODEL ARIMA 2,0,2 Final Estimates of Parameters Type Coef SE Coef T P AR 1 1.6694 0.0897 18.62 0.000 AR 2 -0.6693 0.0897 -7.46 0.000 MA 1 0.6215 0.1097 5.66 0.000 MA 2 0.3660 0.1091 3.36 0.001 Number of observations: 100 Residuals: SS = 5.86690 backforecasts excluded MS = 0.06111 DF = 96 Modified Box-Pierce Ljung-Box Chi-Square statistic Lag 12 24 36 48 Chi-Square 9.7 18.7 31.0 38.5 DF 8 20 32 44 P-Value 0.288 0.542 0.518 0.704 Forecasts from period 100 95 Percent Limits Period Forecast Lower Upper 101 7.04689 6.56226 7.53152 102 7.02274 6.32076 7.72473 103 7.00674 6.22411 7.78937 104 6.99619 6.17826 7.81412 105 6.98928 6.15483 7.82373 106 6.98482 6.14225 7.82739 Normplot of Residuals for Lstok 1 -1 3 2 1 -1 -2 -3 N o rm a l S c o re Residual Normal Probability Plot of the Residuals response is Lstok Residuals vs Order for Lstok 10 20 30 40 50 60 70 80 90 100 -1 1 Observation Order R e si dua l Residuals Versus the Order of the Data response is Lstok Approximate P-Value 0.01 D+: 0.124 D-: 0.133 D : 0.133 Kolmogorov-Smirnov Normality Test N: 100 StDev: 0.243432 Average: -0.0016175 1 -1 .999 .99 .95 .80 .50 .20 .05 .01 .001 P robab ili ty RESI3 Uji kenormalan galat MODEL ARIMA 3,0,0 Final Estimates of Parameters Type Coef SE Coef T P AR 1 1.6739 0.1085 15.43 0.000 AR 2 -0.7778 0.1726 -4.51 0.000 AR 3 0.1038 0.1085 0.96 0.341 Number of observations: 100 Residuals: SS = 7.77631 backforecasts excluded MS = 0.08017 DF = 97 Modified Box-Pierce Ljung-Box Chi-Square statistic Lag 12 24 36 48 Chi-Square 12.0 19.2 33.0 42.6 DF 9 21 33 45 P-Value 0.215 0.570 0.469 0.572 Forecasts from period 100 95 Percent Limits Period Forecast Lower Upper 101 7.11366 6.55860 7.66873 102 7.16985 6.08755 8.25215 103 7.19609 5.63607 8.75611 104 7.20738 5.22966 9.18509 105 7.21169 4.86811 9.55527 106 7.21285 4.54448 9.88123 MODEL ARIMA 0,0,3 Final Estimates of Parameters Type Coef SE Coef T P MA 1 -2.0155 0.0130 -155.35 0.000 MA 2 -1.9421 0.0155 -125.20 0.000 MA 3 -0.8652 0.0411 -21.07 0.000 Number of observations: 100 Residuals: SS = 164.701 backforecasts excluded MS = 1.698 DF = 97 Modified Box-Pierce Ljung-Box Chi-Square statistic Lag 12 24 36 48 Chi-Square 153.0 168.9 227.5 284.6 DF 9 21 33 45 P-Value 0.000 0.000 0.000 0.000 Forecasts from period 100 95 Percent Limits Period Forecast Lower Upper 101 6.17636 3.62186 8.73086 102 4.16801 -1.57940 9.91543 103 1.40093 -6.19159 8.99346 104 0.00000 -7.90767 7.90767 105 0.00000 -7.90767 7.90767 106 0.00000 -7.90767 7.90767 MODEL ARIMA 3,0,3 Final Estimates of Parameters Type Coef SE Coef T P AR 1 1.7723 0.1035 17.12 0.000 AR 2 -0.9367 0.1971 -4.75 0.000 AR 3 0.1643 0.1193 1.38 0.171 MA 1 0.6463 0.0182 35.56 0.000 MA 2 0.1865 0.1247 1.50 0.138 MA 3 0.1699 0.1124 1.51 0.134 Number of observations: 100 Residuals: SS = 5.55919 backforecasts excluded MS = 0.05914 DF = 94 Modified Box-Pierce Ljung-Box Chi-Square statistic Lag 12 24 36 48 Chi-Square 3.7 11.8 23.9 31.6 DF 6 18 30 42 P-Value 0.713 0.859 0.774 0.879 Forecasts from period 100 95 Percent Limits Period Forecast Lower Upper Actual 101 7.05176 6.57501 7.52850 102 7.04469 6.32674 7.76265 103 7.00484 6.17510 7.83458 104 6.97499 6.11350 7.83647 105 6.95825 6.08987 7.82664 106 6.95001 6.08046 7.81956 Model memenuhi Kriteria :

1. ARIMA 2,0,0