tugas_heteroskedastisitas
TRANSCRIPT
Untuk masing masing kelompok :1. Deteksi apakah terdapat heteroskedastisitas menggunakan plot, goldfeld-quant, breush-pagan2. Bila ada, atasi masalah tersebut3. Dikerjakan menggunakan excel4. Dipresentasikan minggu depanKelompok 2Nisa, Danang, dan MeilindaA/ALiquidity(Y)asset structureinventory turnovernet profit/salesNet Profit / Total Assets
10.8250.51166.10.0280.039
21.0390.69766.50.0170.008
30.8540.73052.0-0.121-0.078
41.0650.35168.4-0.102-0.141
51.4420.72554.60.0220.036
61.6200.39029.50.0030.009
71.4770.20663.00.0000.000
81.1080.101105.1-0.019-0.029
93.2240.64940.0-0.028-0.033
101.1600.41872.70.0360.031
111.7110.37875.00.0840.108
120.9360.63436.60.0260.039
131.0760.35849.50.0180.031
141.8290.54130.80.1670.291
151.2700.21186.5-0.021-0.039
161.5050.17477.70.0560.064
171.0350.38748.60.0090.017
181.0750.23790.20.0470.052
191.0330.43699.6-0.020-0.021
201.4680.272104.30.0200.031
212.5210.01345.90.0090.013
221.2180.14558.60.0360.065
230.8030.38641.00.0870.188
241.0390.28278.20.0220.034
252.7680.60948.50.2260.209
260.5930.24837.90.1800.119
273.3640.18757.00.0780.250
281.1010.07154.00.0080.011
291.1550.40869.30.0670.092
301.2420.11677.30.0540.139
311.3830.21932.60.0930.191
321.4780.23681.90.0320.042
331.3500.24559.7-0.078-0.093
343.3860.58196.80.0020.001
354.0160.04859.70.1650.166
362.3330.10981.50.1960.224
370.6540.34962.40.0310.038
382.0310.17193.20.2170.287
391.4850.22571.50.0550.094
401.1420.02253.10.0130.021
412.0670.09660.8-0.111-0.188
421.4080.86379.9-0.023-0.025
431.2800.03311.50.0090.064
443.2910.22673.30.1340.165
452.3080.25739.70.1210.307
463.1800.23250.7-0.023-0.024
470.9410.360100.2-0.027-0.022
480.6030.09348.10.0200.017
494.3880.52285.2-0.009-0.009
501.2810.42592.60.0730.057
512.7680.56188.80.0080.015
521.3620.67546.80.0250.043
533.1910.41081.90.0840.085
540.5110.20646.3-0.200-0.250
552.7060.40248.90.0360.027
561.4580.30141.50.0600.074
574.8120.73596.00.0110.010
581.1940.21625.00.1890.213
591.0890.63026.90.0160.033
601.5010.13516.80.0440.080
613.4840.09815.00.0550.056
622.0820.13430.30.1670.163
631.1870.0141.20.1150.207
643.1670.5366.80.1130.266
652.4980.0157.00.0800.201
661.5490.43418.60.0480.123
673.1760.97815.10.1120.182
683.8370.11615.20.1160.169
694.1740.61132.70.0960.129
701.2440.15028.10.0410.111
711.2830.01245.00.0130.055
720.2050.5616.50.0150.019
731.1370.20112.9-0.024-0.027
741.2250.2967.70.0830.186
751.1680.43727.80.0580.097
761.4390.5095.80.0110.019
771.4770.35938.60.0860.143
782.6350.4934.80.0740.168
791.7420.39140.80.1360.233
801.1640.46927.30.1460.165
811.4610.06433.90.0340.079
821.0970.32823.70.2200.241
831.2920.02737.40.0180.034
842.1940.14812.00.0220.045
850.7740.2750.2-0.005-0.010
860.6610.32711.60.0010.003
875.2000.5863.00.0350.036
881.5750.39320.60.0870.060
891.1440.30713.20.0210.071
900.9390.32127.10.0060.011
911.9920.41823.80.1390.198
921.5580.67131.10.1760.360
931.2860.40631.60.1360.205
941.2860.82430.00.0880.230
950.5090.20415.10.0500.101
965.2000.465135.5-0.200-0.169
975.2000.403142.40.1540.098
981.6460.516124.80.0760.055
992.9400.399168.20.0480.047
1001.4950.329198.80.2790.158
1014.2220.836183.50.0420.033
1021.0560.443330.00.0510.027
1033.4590.463251.20.0010.001
1041.5610.326212.60.0700.064
1051.9250.371192.70.0690.084
1060.9330.096190.80.0010.001
1071.1960.378215.40.0400.043
1081.1760.489302.10.0540.033
1091.2640.226143.90.0520.036
1100.4880.437330.0-0.200-0.078
1111.5660.743150.80.0010.001
1121.9670.596330.00.0010.000
1130.9610.419165.60.0040.001
1140.8650.107120.50.0170.016
1151.1920.323119.5-0.108-0.121
1161.2590.152149.70.0330.032
1171.1630.615127.1-0.033-0.064
1181.2310.629155.10.0340.033
1191.1510.352243.70.0610.045
1204.4780.159127.60.0840.133
1212.0660.735193.5-0.093-0.074
1221.9540.309189.70.0470.029
1231.5770.516140.20.0500.099
1242.6450.413138.70.0080.009
1251.1830.573228.4-0.034-0.009
1261.2540.248198.50.0520.029
1271.5520.110137.50.1640.164
1280.5620.284120.0-0.211-0.075
1291.0900.575136.6-0.106-0.071
1304.6280.432122.10.0200.009
1311.8300.495161.60.0040.002
1322.4400.131330.00.2180.046
1334.6500.166118.70.0670.060
1342.8750.295175.00.1230.067
1351.1300.306214.0-0.200-0.145
1360.7210.655195.9-0.204-0.182
1372.2830.330151.9-0.082-0.065
1381.5990.120203.70.0050.005
1395.2000.465199.40.0280.006
1401.0990.582156.90.0400.029
1411.2370.440265.60.0130.010
1420.8000.297330.00.0780.028
1431.3380.646114.80.0140.011
1. Berdasarkan Plot Y prediksi dengan residu dikuadratkan
Dari plot di atas diketahui bahwa terdapat heteroskrdastisitas karena mempunyai tren naik.
2. Goldfeld-Quant TestLangkah 1: a. Tentukan peubah eksogen yang paling berhubungan dengan ragam galat. Correlations
abs_resx1x2x3x4
abs_resPearson Correlation1.184(*).001.007-.038
Sig. (2-tailed) .028.992.937.648
N143143143143143
x1Pearson Correlation.184(*)1.125-.128-.089
Sig. (2-tailed).028 .137.128.289
N143143143143143
x2Pearson Correlation.001.1251-.179(*)-.340(**)
Sig. (2-tailed).992.137 .033.000
N143143143143143
x3Pearson Correlation.007-.128-.179(*)1.866(**)
Sig. (2-tailed).937.128.033 .000
N143143143143143
x4Pearson Correlation-.038-.089-.340(**).866(**)1
Sig. (2-tailed).648.289.000.000
N143143143143143
* Correlation is significant at the 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed).
Berdasarkan tabel korelasi di atas diketahui bahwa peubah yang memiliki korelasi terbesar adalah peubah X1. Sehingga dapat dikatakan bahwa yang penyebab adanya heterosksedastisitas adalah pada peubah X1.
b. Urutkan pengamatan untuk peubah ini dari yang terbesar ke yang terkecilA/ALiquidity(Y)asset structure (X1)inventory turnover (X2)net profit/sales (X3)Net Profit / Total Assets (X4)
711.2830.012450.0130.055
212.5210.01345.90.0090.013
631.1870.0141.20.1150.207
652.4980.01570.080.201
401.1420.02253.10.0130.021
831.2920.02737.40.0180.034
431.280.03311.50.0090.064
354.0160.04859.70.1650.166
811.4610.06433.90.0340.079
281.1010.071540.0080.011
480.6030.09348.10.020.017
412.0670.09660.8-0.111-0.188
1060.9330.096190.80.0010.001
613.4840.098150.0550.056
81.1080.101105.1-0.019-0.029
1140.8650.107120.50.0170.016
362.3330.10981.50.1960.224
1271.5520.11137.50.1640.164
301.2420.11677.30.0540.139
683.8370.11615.20.1160.169
1381.5990.12203.70.0050.005
1322.440.1313300.2180.046
622.0820.13430.30.1670.163
601.5010.13516.80.0440.08
221.2180.14558.60.0360.065
842.1940.148120.0220.045
701.2440.1528.10.0410.111
1161.2590.152149.70.0330.032
1204.4780.159127.60.0840.133
1334.650.166118.70.0670.06
382.0310.17193.20.2170.287
161.5050.17477.70.0560.064
273.3640.187570.0780.25
731.1370.20112.9-0.024-0.027
950.5090.20415.10.050.101
71.4770.2066300
540.5110.20646.3-0.2-0.25
151.270.21186.5-0.021-0.039
581.1940.216250.1890.213
311.3830.21932.60.0930.191
391.4850.22571.50.0550.094
443.2910.22673.30.1340.165
1091.2640.226143.90.0520.036
463.180.23250.7-0.023-0.024
321.4780.23681.90.0320.042
181.0750.23790.20.0470.052
331.350.24559.7-0.078-0.093
260.5930.24837.90.180.119
1261.2540.248198.50.0520.029
452.3080.25739.70.1210.307
201.4680.272104.30.020.031
850.7740.2750.2-0.005-0.01
241.0390.28278.20.0220.034
1280.5620.284120-0.211-0.075
1342.8750.2951750.1230.067
741.2250.2967.70.0830.186
1420.80.2973300.0780.028
561.4580.30141.50.060.074
1351.130.306214-0.2-0.145
891.1440.30713.20.0210.071
1221.9540.309189.70.0470.029
900.9390.32127.10.0060.011
1151.1920.323119.5-0.108-0.121
1041.5610.326212.60.070.064
860.6610.32711.60.0010.003
821.0970.32823.70.220.241
1001.4950.329198.80.2790.158
1372.2830.33151.9-0.082-0.065
370.6540.34962.40.0310.038
41.0650.35168.4-0.102-0.141
1191.1510.352243.70.0610.045
131.0760.35849.50.0180.031
771.4770.35938.60.0860.143
470.9410.36100.2-0.027-0.022
1051.9250.371192.70.0690.084
111.7110.378750.0840.108
1071.1960.378215.40.040.043
230.8030.386410.0870.188
171.0350.38748.60.0090.017
61.620.3929.50.0030.009
791.7420.39140.80.1360.233
881.5750.39320.60.0870.06
992.940.399168.20.0480.047
552.7060.40248.90.0360.027
975.20.403142.40.1540.098
931.2860.40631.60.1360.205
291.1550.40869.30.0670.092
533.1910.4181.90.0840.085
1242.6450.413138.70.0080.009
101.160.41872.70.0360.031
911.9920.41823.80.1390.198
1130.9610.419165.60.0040.001
501.2810.42592.60.0730.057
1304.6280.432122.10.020.009
661.5490.43418.60.0480.123
191.0330.43699.6-0.02-0.021
751.1680.43727.80.0580.097
1100.4880.437330-0.2-0.078
1411.2370.44265.60.0130.01
1021.0560.4433300.0510.027
1033.4590.463251.20.0010.001
965.20.465135.5-0.2-0.169
1395.20.465199.40.0280.006
801.1640.46927.30.1460.165
1081.1760.489302.10.0540.033
782.6350.4934.80.0740.168
1311.830.495161.60.0040.002
761.4390.5095.80.0110.019
10.8250.51166.10.0280.039
981.6460.516124.80.0760.055
1231.5770.516140.20.050.099
494.3880.52285.2-0.009-0.009
643.1670.5366.80.1130.266
141.8290.54130.80.1670.291
512.7680.56188.80.0080.015
720.2050.5616.50.0150.019
1251.1830.573228.4-0.034-0.009
1291.090.575136.6-0.106-0.071
343.3860.58196.80.0020.001
1401.0990.582156.90.040.029
875.20.58630.0350.036
1121.9670.5963300.0010
252.7680.60948.50.2260.209
694.1740.61132.70.0960.129
1171.1630.615127.1-0.033-0.064
1181.2310.629155.10.0340.033
591.0890.6326.90.0160.033
120.9360.63436.60.0260.039
1431.3380.646114.80.0140.011
93.2240.64940-0.028-0.033
1360.7210.655195.9-0.204-0.182
921.5580.67131.10.1760.36
521.3620.67546.80.0250.043
21.0390.69766.50.0170.008
51.4420.72554.60.0220.036
30.8540.7352-0.121-0.078
574.8120.735960.0110.01
1212.0660.735193.5-0.093-0.074
1111.5660.743150.80.0010.001
941.2860.824300.0880.23
1014.2220.836183.50.0420.033
421.4080.86379.9-0.023-0.025
673.1760.97815.10.1120.182
Langkah 2: Bagi pengamatan terurut menjadi dua sub sampel yang sama besar c pengamatan di tengah dihilangkan 2 sub sampel beranggotakan (n - c) pengamatan Sub sampel I beranggotakan pengamatan dengan nilai-nilai besar
Sub Sampel 1:A/ALiquidity(Y)asset structure (X1)inventory turnover (X2)net profit/sales (X3)Net Profit / Total Assets (X4)
711.2830.012450.0130.055
212.5210.01345.90.0090.013
631.1870.0141.20.1150.207
652.4980.01570.080.201
401.1420.02253.10.0130.021
831.2920.02737.40.0180.034
431.280.03311.50.0090.064
354.0160.04859.70.1650.166
811.4610.06433.90.0340.079
281.1010.071540.0080.011
480.6030.09348.10.020.017
412.0670.09660.8-0.111-0.188
1060.9330.096190.80.0010.001
613.4840.098150.0550.056
81.1080.101105.1-0.019-0.029
1140.8650.107120.50.0170.016
362.3330.10981.50.1960.224
1271.5520.11137.50.1640.164
301.2420.11677.30.0540.139
683.8370.11615.20.1160.169
1381.5990.12203.70.0050.005
1322.440.1313300.2180.046
622.0820.13430.30.1670.163
601.5010.13516.80.0440.08
221.2180.14558.60.0360.065
842.1940.148120.0220.045
701.2440.1528.10.0410.111
1161.2590.152149.70.0330.032
1204.4780.159127.60.0840.133
1334.650.166118.70.0670.06
382.0310.17193.20.2170.287
161.5050.17477.70.0560.064
273.3640.187570.0780.25
731.1370.20112.9-0.024-0.027
950.5090.20415.10.050.101
71.4770.2066300
540.5110.20646.3-0.2-0.25
151.270.21186.5-0.021-0.039
581.1940.216250.1890.213
311.3830.21932.60.0930.191
391.4850.22571.50.0550.094
443.2910.22673.30.1340.165
1091.2640.226143.90.0520.036
463.180.23250.7-0.023-0.024
321.4780.23681.90.0320.042
181.0750.23790.20.0470.052
331.350.24559.7-0.078-0.093
260.5930.24837.90.180.119
1261.2540.248198.50.0520.029
452.3080.25739.70.1210.307
201.4680.272104.30.020.031
850.7740.2750.2-0.005-0.01
241.0390.28278.20.0220.034
1280.5620.284120-0.211-0.075
1342.8750.2951750.1230.067
741.2250.2967.70.0830.186
1420.80.2973300.0780.028
561.4580.30141.50.060.074
1351.130.306214-0.2-0.145
Sub Sampel 2:A/ALiquidity(Y)asset structure (X1)inventory turnover (X2)net profit/sales (X3)Net Profit / Total Assets (X4)
552.7060.40248.90.0360.027
975.20.403142.40.1540.098
931.2860.40631.60.1360.205
291.1550.40869.30.0670.092
533.1910.4181.90.0840.085
1242.6450.413138.70.0080.009
101.160.41872.70.0360.031
911.9920.41823.80.1390.198
1130.9610.419165.60.0040.001
501.2810.42592.60.0730.057
1304.6280.432122.10.020.009
661.5490.43418.60.0480.123
191.0330.43699.6-0.02-0.021
751.1680.43727.80.0580.097
1100.4880.437330-0.2-0.078
1411.2370.44265.60.0130.01
1021.0560.4433300.0510.027
1033.4590.463251.20.0010.001
965.20.465135.5-0.2-0.169
1395.20.465199.40.0280.006
801.1640.46927.30.1460.165
1081.1760.489302.10.0540.033
782.6350.4934.80.0740.168
1311.830.495161.60.0040.002
761.4390.5095.80.0110.019
10.8250.51166.10.0280.039
981.6460.516124.80.0760.055
1231.5770.516140.20.050.099
494.3880.52285.2-0.009-0.009
643.1670.5366.80.1130.266
141.8290.54130.80.1670.291
512.7680.56188.80.0080.015
720.2050.5616.50.0150.019
1251.1830.573228.4-0.034-0.009
1291.090.575136.6-0.106-0.071
343.3860.58196.80.0020.001
1401.0990.582156.90.040.029
875.20.58630.0350.036
1121.9670.5963300.0010
252.7680.60948.50.2260.209
694.1740.61132.70.0960.129
1171.1630.615127.1-0.033-0.064
1181.2310.629155.10.0340.033
591.0890.6326.90.0160.033
120.9360.63436.60.0260.039
1431.3380.646114.80.0140.011
93.2240.64940-0.028-0.033
1360.7210.655195.9-0.204-0.182
921.5580.67131.10.1760.36
521.3620.67546.80.0250.043
21.0390.69766.50.0170.008
51.4420.72554.60.0220.036
30.8540.7352-0.121-0.078
574.8120.735960.0110.01
1212.0660.735193.5-0.093-0.074
1111.5660.743150.80.0010.001
941.2860.824300.0880.23
1014.2220.836183.50.0420.033
421.4080.86379.9-0.023-0.025
673.1760.97815.10.1120.182
Langkah 3: Lakukan analisis regresi untuk Y terhadap semua variabel X, pada masing-masing sub sampel Sub Sampel 1:SUMMARY OUTPUT
Regression Statistics
Multiple R0.392917
R Square0.154383
Adjusted R Square0.091745
Standard Error0.944533
Observations59
ANOVA
dfSSMSFSignificance F
Regression48.795392.1988482.4646830.055852
Residual5448.175680.892142
Total5856.97107
CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept1.6914340.3092515.4694471.19E-061.0714232.3114461.0714232.311446
X Variable 1-1.486471.497575-0.992580.32534-4.488921.515989-4.488921.515989
X Variable 20.0008370.0020140.4158740.67915-0.00320.004875-0.00320.004875
X Variable 32.1699782.8156340.7706890.44425-3.475037.814983-3.475037.814983
X Variable 41.6836852.4255740.6941390.490571-3.17936.546668-3.17936.546668
Sub Sampel 2:SUMMARY OUTPUT
Regression Statistics
Multiple R0.221218
R Square0.048938
Adjusted R Square-0.02023
Standard Error1.38065
Observations60
ANOVA
dfSSMSFSignificance F
Regression45.3946471.3486620.7075150.59025
Residual55104.84071.906194
Total59110.2353
CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept2.19440.908162.4163150.0190250.3744074.0143930.3744074.014393
X Variable 10.1952781.3959890.1398850.889262-2.602352.992902-2.602352.992902
X Variable 2-0.001130.002389-0.471740.638975-0.005910.00366-0.005910.00366
X Variable 37.7519514.8068371.6126930.112535-1.8811717.38507-1.8811717.38507
X Variable 4-5.920014.05997-1.458140.150489-14.05642.216353-14.05642.216353
Langkah 4:Hitung statistik uji F sbb:
Sedangkan titik kritis yang di dapat F0.05(54,55) = 1.56844. karena statistic uji F lebih besar dari titik kritis maka diputuskan untuk menolak H0 sehingga disimpulkan bahwa terdapat heteroskedastisitas.3. Breusch-Pagan LM test
Langkah 1: duga model regresi di atas dan dapatkan penduga residualnya
Langkah 2: menduga auxiliary regression berikut di mana peubah bebas yang digunakan adalah peubah-peubah yang mungkin mempengaruhi ragam galat Peubah eksogen X
SUMMARY OUTPUT
Regression Statistics
Multiple R0.159985748
R Square0.02559544
Adjusted R Square-0.002648171
Standard Error2.321727822
Observations143
ANOVA
dfSSMSFSignificance F
Regression419.540019134.8850050.9062380.462251
Residual138743.8779715.39042
Total142763.4179901
CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept0.8877998380.4859856361.8268030.06989-0.073141.848741-0.073141.848741
X Variable 11.4242849490.9528254371.4948020.137249-0.459743.30831-0.459743.30831
X Variable 2-0.0007645960.002693203-0.28390.776914-0.006090.004561-0.006090.004561
X Variable 34.2037784674.6992132510.8945710.372574-5.0879913.49555-5.0879913.49555
X Variable 4-4.8332810014.149908664-1.164670.246161-13.03893.372348-13.03893.372348
Langkah 3: formulasikan hipotesis nol dan alternatif Hipotesis nol: kasus homokesdastisitas, tidak ada hubungan antara X dan residual
Hipotesis alternatif: kasus heterokesdastisitas, terdapat hubungan antara X dan Residual
Langkah 4: Dapatkan statistik uji berdasarkan koefisien determinasi dari auxiliary regression R2
Sedangkan tiik kritis yang didapatkan = 9.487. karena statistic uji LM lebih kecil daripada titik kritis, diputuskan untuk terima H0 sehingga disimpulkan bahwa tidak ada ksus heteroskedastisitas.Pada kedua uji ini menghasilkan kesimpulan yang berbeda mungkin dikarenakan uji Goldfeld-Quant Test lebih sensitive mendeteksi adanya heteroskedastisitas.4. Cara mengatasiWeighted least squareMetode ini menggunakan pembobot Zi, di mana Zi adalah nilai Z score dari X1i karena X1 yang memiliki korelasi terbesar terhadap residu. Transformasi dilakukan dengan cara membagi persamaan regresi dengan zt
A/ALiquidity(Y)asset structure (X1)inventory turnover (X2)net profit/sales (X3)Net Profit / Total Assets (X4)Z score untuk X1
11.1632591.1632591.1632591.1632591.1632591.163259
20.6482560.43487441.490880.0106070.0049911.602762
30.4848710.41446829.52374-0.0687-0.044291.761294
4-17.9206-5.90623-1150.961.7163412.37259-0.05943
50.8300360.4173231.428540.0126640.0207221.737274
612.663373.04859230.59850.0234510.0703520.127928
7-1.95367-0.27248-83.332100-0.75601
8-0.87906-0.08013-83.3840.0150740.023008-1.26043
92.3495650.47297429.15093-0.02041-0.024051.372169
104.4200491.592742277.01520.1371740.1181220.262441
1124.345555.3785041067.1631.1952231.5367150.07028
120.719940.48765228.15150.0199980.0299971.300109
13-41.7045-13.8756-1918.56-0.69766-1.20152-0.0258
142.1433560.63398336.093690.1957030.3410150.853335
15-1.73499-0.28825-118.1710.0286890.053279-0.73199
16-1.65432-0.19126-85.409-0.06156-0.07035-0.90974
179.1176623.409213428.13370.0792840.1497590.113516
18-1.77075-0.39039-148.578-0.07742-0.08565-0.60709
192.9606251.249596285.4581-0.05732-0.060190.348913
20-3.34437-0.61967-237.615-0.04556-0.07062-0.43895
21-1.49775-0.00772-27.2697-0.00535-0.00772-1.68319
22-1.16104-0.13822-55.8597-0.03432-0.06196-1.04906
237.3864953.550669377.14360.800281.7293410.108712
24-2.65793-0.7214-200.048-0.05628-0.08698-0.39091
252.3457470.51609841.101410.1915240.1771171.180008
26-1.06993-0.44746-68.3816-0.32477-0.21471-0.55424
27-3.97032-0.2207-67.2735-0.09206-0.29506-0.84729
28-0.78388-0.05055-38.4464-0.0057-0.00783-1.40455
295.3871191.902982323.22710.31250.4291040.2144
30-1.04513-0.09761-65.0469-0.04544-0.11697-1.18837
31-1.99406-0.31576-47.0039-0.13409-0.27539-0.69356
32-2.41546-0.38569-133.847-0.0523-0.06864-0.61189
33-2.37402-0.43084-104.9850.1371660.163544-0.56865
343.2386560.55571792.587670.0019130.0009561.045496
35-2.65074-0.03168-39.4047-0.10891-0.10957-1.51505
36-1.90916-0.0892-66.6939-0.16039-0.18331-1.222
37-9.47321-5.05528-903.866-0.44904-0.55043-0.06904
38-2.19769-0.18503-100.849-0.23481-0.31055-0.92415
39-2.23397-0.33848-107.562-0.08274-0.14141-0.66474
40-0.69636-0.01342-32.379-0.00793-0.01281-1.63995
41-1.60924-0.07474-47.33530.0864180.146366-1.28445
420.5866110.35954933.28849-0.00958-0.010422.400229
43-0.8065-0.02079-7.24589-0.00567-0.04032-1.58711
44-4.98688-0.34246-111.072-0.20305-0.25003-0.65993
45-4.51658-0.50293-77.6898-0.23679-0.60078-0.51101
46-5.03877-0.36761-80.3350.0364440.038028-0.63111
47-58.1131-22.2324-6188.021.6674321.358648-0.01619
48-0.46425-0.0716-37.0323-0.0154-0.01309-1.29887
495.7580880.684987111.8024-0.01181-0.011810.762058
504.3266991.435478312.76530.2465640.1925230.296069
512.9154790.5908993.531260.0084260.0157990.949415
520.9097750.4508831.260990.0166990.0287231.497073
5314.2451.830289365.61130.3749860.379450.224008
54-0.67592-0.27248-61.24250.2645460.330683-0.75601
5514.581612.166226263.50360.193990.1454930.185576
56-4.86601-1.00457-138.504-0.20025-0.24697-0.29963
572.6953240.41169253.772040.0061610.0056011.785314
58-1.68651-0.3051-35.3122-0.26696-0.30086-0.70797
590.8501880.49184521.000980.0124910.0257631.280892
60-1.36816-0.12305-15.3131-0.04011-0.07292-1.0971
61-2.73288-0.07687-11.7661-0.04314-0.04393-1.27485
62-1.88946-0.12161-27.4979-0.15156-0.14793-1.1019
63-0.70723-0.00834-0.71497-0.06852-0.12333-1.67838
643.8188160.6463178.1995410.1362570.3207470.829315
65-1.49261-0.00896-4.18265-0.0478-0.1201-1.67358
664.5652171.27908654.817970.1414660.3625060.339305
671.0756290.3312235.1139790.0379320.0616392.952691
68-3.22878-0.09761-12.7906-0.09761-0.14221-1.18837
693.5086950.51361127.487860.0806980.1084381.189616
70-1.21362-0.14634-27.4137-0.04-0.10829-1.02504
71-0.76008-0.00711-26.6589-0.0077-0.03258-1.68799
720.2159220.590896.846320.0157990.0200120.949415
73-1.45763-0.25768-16.53780.0307680.034614-0.78003
74-3.78496-0.91457-23.7911-0.25645-0.5747-0.32365
753.3020761.23545178.593930.1639730.2742310.353717
762.0568710.7275528.2903780.0157230.0271580.699606
77-70.3448-17.098-1838.39-4.0959-6.81063-0.021
784.2312870.791667.7078480.1188290.2697750.622742
7913.124192.945785307.38631.0246211.7554170.132732
802.2938430.92423753.798880.2877160.3251580.507445
81-1.01587-0.0445-23.5714-0.02364-0.05493-1.43818
82-6.45594-1.93031-139.476-1.29472-1.4183-0.16992
83-0.79954-0.01671-23.1446-0.01114-0.02104-1.61593
84-2.12054-0.14304-11.5982-0.02126-0.04349-1.03464
85-1.82317-0.64777-0.47110.0117780.023555-0.42453
86-3.78308-1.87151-66.39-0.00572-0.01717-0.17473
874.8620140.5479122.8050080.0327250.033661.069516
8811.065052.760994144.72380.6112120.4215260.14234
89-4.22443-1.13365-48.7435-0.07755-0.26218-0.27081
90-4.61313-1.57701-133.137-0.02948-0.05404-0.20355
917.5902911.59274290.687220.5296440.7544570.262441
921.0542290.45403621.043980.1190910.2435961.477857
936.2795331.982496154.30270.6640871.0010140.204792
940.5811450.37236713.557040.0397670.1039372.212872
95-0.66482-0.26645-19.7226-0.06531-0.13192-0.76562
9610.650730.952421277.5335-0.40964-0.346150.488229
9727.313762.116816747.97680.8089070.5147590.19038
982.2448480.703731170.20480.103650.075010.733234
9917.176492.331095982.68230.2804330.274590.171164
100-9.05418-1.99252-1203.99-1.68971-0.9569-0.16512
1011.8594860.36819880.818480.0184980.0145342.27052
1022.7604891.158046862.65270.1333190.0705810.382541
1037.2270070.967362524.84080.0020890.0020890.478621
104-8.69496-1.81586-1184.21-0.38991-0.35649-0.17953
10552.5215310.122335257.6091.882592.2918480.036652
106-0.72638-0.07474-148.546-0.00078-0.00078-1.28445
10717.01775.3785043064.8930.5691540.611840.07028
1081.948550.810239500.55850.0894740.0546790.603526
109-1.91535-0.34246-218.053-0.0788-0.05455-0.65993
1101.3796341.235451932.9496-0.56542-0.220520.353717
1110.8586720.40740382.686930.0005480.0005481.823747
1121.7600910.533307295.28730.00089501.117556
1133.5959581.567852619.65720.0149680.0037420.267245
114-0.70233-0.08688-97.8395-0.0138-0.01299-1.23161
115-6.14619-1.66545-616.1660.556870.6239-0.19394
116-1.23987-0.14969-147.425-0.0325-0.03151-1.01543
1170.9620860.508755105.1428-0.0273-0.052941.208832
1180.9646670.492913121.54330.0266440.025861.276088
119-21.071-6.44397-4461.35-1.11671-0.8238-0.05462
120-4.56101-0.16195-129.965-0.08556-0.13547-0.9818
1211.1572190.411692108.3843-0.05209-0.041451.785314
122-7.48093-1.18301-726.27-0.17994-0.11103-0.2612
1232.1507450.703731191.20760.0681910.1350180.733234
12411.093851.732234581.74550.0335540.0377480.23842
1251.1747030.568981226.798-0.03376-0.008941.007063
126-2.26255-0.44746-358.146-0.09382-0.05232-0.55424
127-1.27506-0.09037-112.964-0.13474-0.13474-1.2172
128-1.47391-0.74482-314.7140.5533730.196697-0.3813
1291.0721260.565571134.36-0.10426-0.069841.016671
13014.037141.310295370.34030.0606620.0272980.329697
1312.8939670.782794255.55470.0063260.0031630.63235
132-2.18577-0.11735-295.616-0.19529-0.04121-1.11631
133-4.90417-0.17507-125.188-0.07066-0.06328-0.94817
134-8.75313-0.89815-532.799-0.37448-0.20399-0.32845
135-4.1-1.11027-776.4610.7256640.526107-0.27561
1360.5146350.467526139.8294-0.14561-0.129911.400993
137-14.2409-2.05847-947.5210.5114990.405457-0.16031
138-1.36765-0.10264-174.228-0.00428-0.00428-1.16916
13910.650730.952421408.41460.057350.0122890.488229
1401.0463680.554128149.38590.0380840.0276111.0503
1413.3602361.195233721.48640.0353140.0271640.368129
142-2.50905-0.93148-1034.98-0.24463-0.08782-0.31885
1430.9854490.47578584.551230.0103110.0081021.357757
Dari transformasi ini didapatkan persamaan:SUMMARY OUTPUT
Regression Statistics
Multiple R0.957879
R Square0.917532
Adjusted R Square0.915142
Standard Error3.334469
Observations143
ANOVA
dfSSMSFSignificance F
Regression417071.354267.837383.84381.08E-73
Residual1381534.37811.11868
Total14218605.73
CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept0.0916990.2795820.3279850.74342-0.461120.644518-0.461120.644518
X Variable 12.7730630.22277112.448062.57E-242.3325773.2135482.3325773.213548
X Variable 20.0015540.0006662.3325220.0211190.0002370.0028710.0002370.002871
X Variable 33.5289771.9249091.8333210.06891-0.277157.335107-0.277157.335107
X Variable 40.3941811.5248090.2585120.796397-2.620833.409192-2.620833.409192
Setelah didapatkan persamaan tersebut di atas maka dilakukan pengujian terhadap ada atau tidaknya heteroskedastisitas dengan menggunakan uji Goldfeld-Quant seperti pada langkah sebelumnya. Berikut adalah table ANOVA yang di dapat pada kedua sub sampel:sub sampel 1
ANOVA
dfSSMSFSignificance F
Regression48947.0442236.761425.66054E-40
Residual54283.75925.2548
Total589230.803
sub sampel 2
ANOVA
dfSSMSFSignificance F
Regression43445.141861.285361.545161.16E-19
Residual55769.6913.99436
Total594214.831
Statistik uji F:
Sedangkan titik kritis yang di dapat F0.05(54,55) = 1.56844. karena statistic uji F lebih besar dari titik kritis maka diputuskan untuk menolak H0 sehingga disimpulkan bahwa terdapat heteroskedastisitas.jadi metode WLS yang digunakan belum dapat menangani kasus heteroskedastisitas pada data.