Analisis Efisiensi Usahatani Padi Sawah di Desa Sumber Tani Kecamatan Talawi Kabupaten Batu Bara : Suatu Pendekatan Stochastic Frontier
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Lampiran 7. Uji Linearitas Ramsey Reset Test Persamaan Produksi
msey RESET Test: Data Level
statistic
g likelihood ratio
1.917704
Prob. F(2,57)
4.232846
Prob. Chi-Square(2)
0.1563
0.1205
st Equation:
pendent Variable: Y
Method: Least Squares
te: 08/19/15 Time: 08:19
mple: 1 65
luded observations: 65
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
X1
X2
X3
X4
X5
FITTED^2
FITTED^3
9937.751
-93.71989
-12.40053
72.80448
-9.293042
-0.776462
0.001233
-9.79E-08
5971.710
62.56536
8.239320
48.87239
6.227232
0.512677
0.000631
5.01E-08
1.664138
-1.497952
-1.505042
1.489685
-1.492323
-1.514524
1.955263
-1.954356
0.1016
0.1397
0.1378
0.1418
0.1411
0.1354
0.0555
0.0556
squared
Adjusted R-squared
.E. of regression
um squared resid
g likelihood
statistic
rob(F-statistic)
0.521944
Mean dependent var
0.463235
S.D. dependent var
1386.414
Akaike info criterion
1.10E+08
Schwarz criterion
-558.2035
Hannan-Quinn criter.
8.890416
Durbin-Watson stat
0.000000
3997.769
1892.349
17.42165
17.68926
17.52724
2.044358
Nilai signifikansi fittet^2 0,050,1 maka H0 : model linear tidak dapat
ditolak.
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Lampiran 9.
Uji Linearitas Ramsey
Mempengaruhi Efisiensi
ReseT
Test
Faktor-Faktor
yang
msey RESET Test:
statistic
g likelihood ratio
1.798786
Prob. F(2,57)
3.978231
Prob. Chi-Square(2)
0.1748
0.1368
st Equation:
pendent Variable: TE
Method: Least Squares
te: 08/19/15 Time: 09:27
mple: 1 65
luded observations: 65
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
Z1
Z2
Z3
Z4
Z5
FITTED^2
FITTED^3
-0.022835
0.000432
-0.000337
0.001070
-0.012628
-0.076315
4.255372
-3.968744
3.918433
0.007465
0.002556
0.011556
0.265619
2.830830
14.16755
8.442903
-0.005828
0.057890
-0.131686
0.092576
-0.047543
-0.026958
0.300361
-0.470069
0.9954
0.9540
0.8957
0.9266
0.9622
0.9786
0.7650
0.6401
squared
Adjusted R-squared
.E. of regression
um squared resid
g likelihood
statistic
rob(F-statistic)
0.601918
Mean dependent var
0.553031
S.D. dependent var
0.152469
Akaike info criterion
1.325067
Schwarz criterion
34.28902
Hannan-Quinn criter.
12.31239
Durbin-Watson stat
0.000000
0.586449
0.228057
-0.808893
-0.541276
-0.703301
1.727892
Nilai fitted^2 adalah 0,7>0,1 maka H0 : model linear tidak dapat ditolak.
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Lampiran 10. Uji Normalitas Kolmogorof-Smirnov Residual Data Produksi
Regression
[DataSet0]
Variabl es Entered/Removedb
Model
1
Variables
Entered
lnx5, lnx1,
lnx2,a lnx4,
lnx3
Variables
Remov ed
Method
.
Enter
a. All requested v ariables entered.
b. Dependent Variable: lny
Model Summaryb
Model
1
R
,684a
R Square
,468
Adjusted
R Square
,423
St d. Error of
the Estimate
,48494
a. Predictors: (Constant), lnx5, lnx1, lnx2, lnx4, lnx3
b. Dependent Variable: lny
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
12,213
13,875
26,088
df
5
59
64
Mean Square
2,443
,235
F
10,387
Sig.
,000a
t
-1,202
1,814
3,516
-1,649
3,861
1,692
Sig.
,234
,075
,001
,104
,000
,096
a. Predictors: (Const ant), lnx5, lnx1, lnx2, lnx4, lnx3
b. Dependent Variable: lny
Coeffi ci entsa
Model
1
(Constant)
lnx1
lnx2
lnx3
lnx4
lnx5
Unstandardized
Coef f icients
B
St d. Error
-1,826
1,520
,345
,190
,743
,211
-,208
,126
,477
,124
,193
,114
St andardized
Coef f icients
Beta
,180
,356
-,172
,397
,186
a. Dependent Variable: lny
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Residual s Statisti csa
Predicted Value
Residual
Std. Predicted Value
Std. Residual
Minimum
6,9772
-1,66563
-2,660
-3,435
Maximum
8,8385
1,11625
1,601
2,302
Mean
8,1393
,00000
,000
,000
Std. Dev iat ion
,43684
,46561
1,000
,960
N
65
65
65
65
a. Dependent Variable: lny
NPAR TESTS
/K-S(NORMAL)= RES_1
/MISSING ANALYSIS.
NPar Tests
[DataSet0]
One-Sample Kolmogorov-Smirnov Test
N
Normal Parameters a,b
Most Extreme
Dif f erences
Mean
Std. Dev iat ion
Absolute
Positiv e
Negativ e
Kolmogorov -Smirnov Z
Asy mp. Sig. (2-tailed)
Unstandardiz
ed Residual
65
,0000000
,46560869
,090
,064
-,090
,723
,673
a. Test distribution is Normal.
b. Calculated f rom data.
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Lampiran 11. Uji Normalitas Kolmogorof-Smirnov Residual Data Biaya Produksi
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT C
/METHOD=ENTER PX1 PX2 PX3 PX4 PX5 Y
/SAVE RESID .
Regression
[DataSet0]
Variabl es Entered/Removedb
Model
1
Variables
Entered
Produksi,
Harga
pupuk,
Harga
bibit , Sewa
traktor,
Harga
pestisida,
Upah
tenaga
a
kerja
Variables
Remov ed
.
Method
Enter
a. All requested v ariables entered.
b. Dependent Variable: Biay a produksi
Model Summaryb
Model
1
R
,738a
R Square
,545
Adjusted
R Square
,498
St d. Error of
the Estimate
2280861,12
a. Predictors: (Constant), Produksi, Harga pupuk, Harga
bibit , Sewa traktor, Harga pestisida, Upah t enaga kerja
b. Dependent Variable: Biay a produksi
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
3,6E+014
3,0E+014
6,6E+014
df
6
58
64
Mean Square
6,024E+013
5,202E+012
F
11,579
Sig.
,000a
a. Predictors: (Const ant), Produksi, Harga pupuk, Harga bibit, Sewa trakt or, Harga
pestisida, Upah tenaga kerja
b. Dependent Variable: Biay a produksi
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Coeffi cientsa
Model
1
(Constant)
Harga bibit
Upah tenaga kerja
Sewa traktor
Harga pupuk
Harga pestisida
Produksi
Unstandardized
Coef f icients
B
St d. Error
849827,7
1976003
85,484
106,824
152,512
52,017
25,140
21,350
1222,945
478,024
5403,609
2980,723
602,978
197,978
St andardized
Coef f icients
Beta
t
,430
,800
2,932
1,178
2,558
1,813
3,046
,075
,346
,108
,236
,170
,354
Sig.
,669
,427
,005
,244
,013
,075
,003
a. Dependent Variable: Biay a produksi
Residual s Statisti csa
Predicted Value
Residual
Std. Predicted Value
Std. Residual
Minimum
7830103
-4438223
-2,222
-1,946
Maximum
2E+007
8795421
1,982
3,856
Mean
1E+007
,00000
,000
,000
Std. Dev iat ion
2376447,147
2171315,101
1,000
,952
N
65
65
65
65
a. Dependent Variable: Biay a produksi
NPAR TESTS
/K-S(NORMAL)= RES_1
/MISSING ANALYSIS.
NPar Tests
[DataSet0]
One-Sample Kolmogorov-Smirnov Test
N
Normal Parameters a,b
Most Extreme
Dif f erences
Mean
Std. Dev iat ion
Absolute
Positiv e
Negativ e
Kolmogorov -Smirnov Z
Asy mp. Sig. (2-tailed)
Unstandardiz
ed Residual
65
,0000000
2171315,101
,128
,128
-,079
1,030
,239
a. Test distribution is Normal.
b. Calculated f rom data.
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Lampiran 12. Uji Normalitas Kolmogorof-Smirnov Residual Faktor-Faktor yang
Mempengaruhi Efisiensi
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT te
/METHOD=ENTER z1 z2 z3 z4 z5
/SAVE RESID .
Regression
[DataSet0]
Variabl es Entered/Removedb
Model
1
Variables
Entered
serangan
hama,
pengalam
an bertani,
jumlah
lahan y ang
diusahaka
n, tingkat
pendidika
n,
ketersediaa
an modal
Variables
Remov ed
Method
.
Enter
a. All requested v ariables entered.
b. Dependent Variable: ef isiensi teknis
Model Summaryb
Model
1
R
,734a
R Square
,539
Adjusted
R Square
,500
St d. Error of
the Estimate
,15699
a. Predictors: (Constant), serangan hama, pengalaman
bertani, jumlah lahan y ang diusahakan, tingkat
pendidikan, ketersediaan modal
b. Dependent Variable: ef isiensi teknis
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
1,701
1,454
3,155
df
5
59
64
Mean Square
,340
,025
F
13,806
Sig.
,000a
a. Predictors: (Const ant), serangan hama, pengalaman bertani, jumlah lahan y ang
diusahakan, tingkat pendidikan, ketersediaan modal
b. Dependent Variable: ef isiensi teknis
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Coeffici entsa
Model
1
(Constant)
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
Unstandardized
Coef f icients
B
Std. Error
,674
,090
,002
,007
,001
,001
,001
,001
Standardized
Coef f icients
Beta
,026
,042
,248
t
7,504
,274
,456
2,422
Sig.
,000
,785
,650
,019
-,042
,016
-,245
-2,580
,012
-,343
,044
-,758
-7,791
,000
a. Dependent Variable: ef isiensi t eknis
Residual s Statisti csa
Predicted Value
Residual
Std. Predicted Value
Std. Residual
Minimum
,3395
-,30384
-1,558
-1,935
Maximum
,8234
,40674
1,410
2,591
Mean
,5935
,00000
,000
,000
Std. Dev iat ion
,16304
,15073
1,000
,960
N
65
65
65
65
a. Dependent Variable: ef isiensi t eknis
NPAR TESTS
/K-S(NORMAL)= RES_1
/MISSING ANALYSIS.
NPar Tests
[DataSet0]
One-Sample Kolmogorov-Smirnov Test
N
Normal Parameters a,b
Most Extrem e
Dif f erences
Mean
Std. Dev iat ion
Absolute
Positiv e
Negativ e
Kolmogorov -Smirnov Z
Asy mp. Sig. (2-tailed)
Unstandardiz
ed Residual
65
,0000000
,15073485
,069
,069
-,058
,559
,914
a. Test distribution is Normal.
b. Calculated f rom data.
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Lampiran 13. Hasil Analisis Regresi Persamaan Produksi
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT lny
/METHOD=ENTER x1 x2 x3 x4 x5
/SCATTERPLOT=(*ZPRED ,*SRESID )
/RESIDUALS DURBIN HIST(ZRESID) NORM(ZRESID)
/SAVE RESID .
Regression
[DataSet0]
Descriptive Statistics
produksi
bibit
tenaga kerja
traktor
pupuk
pestisida
Mean
8,1393
62,3692
499,0923
29,2923
706,5077
3915,6154
St d. Dev iation
,63845
19,63620
130,12034
18,56048
307,99204
2154,34878
N
65
65
65
65
65
65
Correlati ons
Pearson Correlation
Sig. (1-tailed)
N
produksi
bibit
tenaga kerja
traktor
pupuk
pestisida
produksi
bibit
tenaga kerja
traktor
pupuk
pestisida
produksi
bibit
tenaga kerja
traktor
pupuk
pestisida
produksi
1,000
,330
,355
-,064
,486
,379
.
,004
,002
,307
,000
,001
65
65
65
65
65
65
bibit
,330
1,000
,021
-,099
,067
,173
,004
.
,434
,216
,297
,084
65
65
65
65
65
65
tenaga kerja
,355
,021
1,000
,221
,126
,221
,002
,434
.
,039
,159
,039
65
65
65
65
65
65
traktor
-,064
-,099
,221
1,000
,110
,135
,307
,216
,039
.
,191
,141
65
65
65
65
65
65
pupuk
,486
,067
,126
,110
1,000
,294
,000
,297
,159
,191
.
,009
65
65
65
65
65
65
pestisida
,379
,173
,221
,135
,294
1,000
,001
,084
,039
,141
,009
.
65
65
65
65
65
65
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Variabl es Entered/Removedb
Model
1
Variables
Entered
pestisida,
traktor,
bibit ,
tenaga
kerja, a
pupuk
Variables
Remov ed
Method
.
Enter
a. All requested v ariables entered.
b. Dependent Variable: produksi
Model Summaryb
Model
1
R
,678a
Adjusted
R Square
,414
R Square
,459
St d. Error of
the Estimate
,48891
DurbinWat son
2,015
a. Predictors: (Constant), pestisida, trakt or, bibit, tenaga kerja, pupuk
b. Dependent Variable: produksi
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
11,985
14,103
26,088
df
Mean Square
2,397
,239
5
59
64
F
10,028
Sig.
,000a
a. Predictors: (Const ant), pestisida, traktor, bibit, tenaga kerja, pupuk
b. Dependent Variable: produksi
Coeffi ci entsa
Model
1
(Constant)
bibit
tenaga kerja
traktor
pupuk
pestisida
Unstandardized
Coef f icients
B
St d. Error
6,289
,328
,008
,003
,001
,000
-,006
,003
,001
,000
5,22E-005
,000
St andardized
Coef f icients
Beta
,249
,298
-,173
,399
,176
t
19,170
2,540
2,977
-1,734
3,963
1,697
Sig.
,000
,014
,004
,088
,000
,095
Zero-order
Correlations
Part ial
,330
,355
-,064
,486
,379
,314
,361
-,220
,459
,216
Part
,243
,285
-,166
,379
,162
Collinearity Statistics
Tolerance
VI F
,954
,912
,925
,905
,849
1,048
1,097
1,081
1,105
1,177
a. Dependent Variable: produksi
a
Colli neari ty Diagnostics
Model
1
Dimension
1
2
3
4
5
6
Eigenv alue
5,370
,251
,164
,123
,067
,024
Condit ion
Index
1,000
4,626
5,717
6,613
8,939
14,878
(Constant)
,00
,00
,02
,00
,01
,96
bibit
,00
,02
,07
,09
,51
,30
Variance Proportions
tenaga kerja
traktor
,00
,01
,00
,84
,02
,02
,01
,00
,45
,12
,52
,01
pupuk
,00
,02
,00
,90
,03
,05
pestisida
,01
,06
,85
,07
,00
,01
a. Dependent Variable: produksi
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Residual s Stati sticsa
Predicted Value
St d. Predicted Value
St andard Error of
Predicted Value
Adjusted Predict ed Value
Residual
St d. Residual
St ud. Residual
Delet ed Residual
St ud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Lev erage Value
Minimum
7,2313
-2,098
Maximum
9,1066
2,235
Mean
8,1393
,000
St d. Dev iation
,43274
1,000
N
,065
,246
,144
,038
65
7,1445
-1,83926
-3,762
-4,126
-2,21246
-4,850
,163
,000
,003
9,2334
1,08847
2,226
2,347
1,21013
2,444
15,188
,576
,237
8,1457
,00000
,000
-,006
-,00634
-,018
4,923
,022
,077
,43539
,46942
,960
1,018
,52817
1,078
3,302
,073
,052
65
65
65
65
65
65
65
65
65
65
65
a. Dependent Variable: produksi
Charts
Histogram
Dependent Variable: produksi
25
Frequency
20
15
10
5
Mean =-6.8
Std. Dev.
N =6
0
-4
-2
0
2
Regression Standardized Residual
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Normal P-P Plot of Regression Standardized Residual
Dependent Variable: produksi
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Scatterplot
Dependent Variable: produksi
Regression Standardized Predicted
Value
3
2
1
0
-1
-2
-3
-4
-2
0
2
Regression Studentized Residual
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Lampiran 14. Regresi Biaya Produksi
[DataSet0]
Descriptive Statistics
Mean
1E+007
6727,1692
29268,12
44799,49
2165,3538
192,0308
3997,7692
biay a produksi
harga bibit
upah tenaga kerja
sewa t raktor
harga pupuk
harga pestisida
produksi
St d. Dev iation
3219023,192
2806,61996
7296,13403
13826,79671
621,96120
101,13774
1892,34860
N
65
65
65
65
65
65
65
Correlati ons
Pearson Correlation
Sig. (1-tailed)
N
biay a produksi
harga bibit
upah tenaga kerja
sewa t raktor
harga pupuk
harga pestisida
produksi
biay a produksi
harga bibit
upah tenaga kerja
sewa t raktor
harga pupuk
harga pestisida
produksi
biay a produksi
harga bibit
upah tenaga kerja
sewa t raktor
harga pupuk
harga pestisida
produksi
biay a
produksi
1,000
-,077
,570
,261
,257
,312
,597
.
,272
,000
,018
,019
,006
,000
65
65
65
65
65
65
65
harga bibit
-,077
1,000
-,233
,043
,088
-,191
-,180
,272
.
,031
,368
,244
,064
,076
65
65
65
65
65
65
65
upah tenaga
kerja
,570
-,233
1,000
,090
-,092
,165
,638
,000
,031
.
,239
,232
,095
,000
65
65
65
65
65
65
65
sewa t raktor
,261
,043
,090
1,000
,165
,140
,158
,018
,368
,239
.
,094
,132
,105
65
65
65
65
65
65
65
harga pupuk
,257
,088
-,092
,165
1,000
,180
-,007
,019
,244
,232
,094
.
,076
,479
65
65
65
65
65
65
65
harga
pestisida
,312
-,191
,165
,140
,180
1,000
,118
,006
,064
,095
,132
,076
.
,175
65
65
65
65
65
65
65
produksi
,597
-,180
,638
,158
-,007
,118
1,000
,000
,076
,000
,105
,479
,175
.
65
65
65
65
65
65
65
Variabl es Entered/Removedb
Model
1
Variables
Entered
produksi,
harga
pupuk,
harga bibit,
sewa
traktor,
harga
pestisida,
upah
tenaga
a
kerja
Variables
Remov ed
.
Method
Enter
a. All requested v ariables entered.
b. Dependent Variable: biay a produksi
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Model Summaryb
Model
1
R
,738a
Adjusted
R Square
,498
R Square
,545
St d. Error of
the Estimate
2280861,12
DurbinWat son
1,726
a. Predictors: (Constant), produksi, harga pupuk, harga bibit , sewa t raktor,
harga pestisida, upah tenaga kerja
b. Dependent Variable: biay a produksi
ANOVAb
Model
1
Sum of
Squares
3,6E+014
3,0E+014
6,6E+014
Regression
Residual
Total
df
6
58
64
Mean Square
6,024E+013
5,202E+012
F
11,579
Sig.
,000a
a. Predictors: (Const ant), produksi, harga pupuk, harga bibit, sewa traktor, harga
pestisida, upah tenaga kerja
b. Dependent Variable: biay a produksi
Coefficientsa
Model
1
(Constant)
harga bibit
upah tenaga kerja
sewa traktor
harga pupuk
harga pestisida
produksi
Unstandardized
Coeff icients
B
Std. Error
849827,7
1976003
85,484
106,824
152,512
52,017
25,140
21,350
1222,945
478,024
5403,609
2980,723
602,978
197,978
Standardized
Coeff icients
Beta
t
,075
,346
,108
,236
,170
,354
,430
,800
2,932
1,178
2,558
1,813
3,046
Sig.
,669
,427
,005
,244
,013
,075
,003
Zero-order
Correlations
Partial
-,077
,570
,261
,257
,312
,597
,105
,359
,153
,318
,232
,371
Collinearity Statistics
Tolerance
VIF
Part
,071
,260
,104
,227
,161
,270
,904
,564
,933
,920
,894
,579
1,106
1,772
1,072
1,087
1,118
1,727
a. Dependent Variable: biay a produksi
a
Collinearity Diagnostics
Model
1
Dimension
1
2
3
4
5
6
7
Eigenvalue
6,375
,228
,186
,085
,065
,045
,014
Condition
Index
1,000
5,286
5,849
8,646
9,868
11,902
21,078
(Constant)
,00
,00
,00
,00
,00
,08
,91
harga bibit
,00
,29
,01
,47
,03
,07
,14
Variance Proportions
upah tenaga
sewa traktor harga pupuk
kerja
,00
,00
,00
,00
,00
,01
,01
,00
,01
,00
,33
,14
,00
,55
,54
,34
,06
,13
,64
,05
,17
harga
pestisida
,00
,25
,47
,22
,03
,03
,00
produksi
,00
,06
,25
,08
,00
,47
,13
a. Dependent Variable: biaya produksi
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Residual s Stati sticsa
Predicted Value
St d. Predicted Value
St andard Error of
Predicted Value
Adjusted Predict ed Value
Residual
St d. Residual
St ud. Residual
Delet ed Residual
St ud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Lev erage Value
Minimum
7830103
-2,222
Maximum
2E+007
1,982
Mean
1E+007
,000
St d. Dev iation
2376447,147
1,000
N
325461,9
1264945
719634,4
207458,554
65
8172192
-4438223
-1,946
-2,063
-4988842
-2,125
,318
,000
,005
2E+007
8795421
3,856
3,977
9355220
4,623
18,700
,144
,292
1E+007
,00000
,000
-,009
-44706,7
,003
5,908
,016
,092
2405054,439
2171315,101
,952
1,003
2412998,785
1,059
3,978
,028
,062
65
65
65
65
65
65
65
65
65
65
65
a. Dependent Variable: biay a produksi
Charts
Histogram
Dependent Variable: biaya produksi
20
Frequency
15
10
5
Mean =-2.2
Std. Dev. =
N =6
0
-2
-1
0
1
2
3
4
Regression Standardized Residual
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Normal P-P Plot of Regression Standardized Residual
Dependent Variable: biaya produksi
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Scatterplot
Dependent Variable: biaya produksi
Regression Standardized Predicted
Value
2
1
0
-1
-2
-3
-2
0
2
Regression Studentized Residual
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Lampiran 15. Efisiensi Teknis
Output from the program FRONTIER (Version 4.1c)
instruction file = terminal
data file =
logln.txt
Error Components Frontier (see B&C 1992)
The model is a production function
The dependent variable is logged
the ols estimates are :
coefficient
standard-error
t-ratio
beta 0
0.62890432E+01 0.32806483E+00 0.19170123E+02
beta 1
0.80937646E-02 0.31863093E-02 0.25401692E+01
beta 2
0.14644466E-02 0.49188918E-03 0.29771881E+01
beta 3
-0.59369192E-02 0.34242270E-02 -0.17337984E+01
beta 4
0.82661525E-03 0.20855789E-03 0.39634812E+01
beta 5
0.52222441E-04 0.30780392E-04 0.16966139E+01
sigma-squared 0.23903429E+00
log likelihood function = -0.42571065E+02
the estimates after the grid search were :
beta 0
0.67575805E+01
beta 1
0.80937646E-02
beta 2
0.14644466E-02
beta 3
-0.59369192E-02
beta 4
0.82661525E-03
beta 5
0.52222441E-04
sigma-squared 0.43649673E+00
gamma
0.79000000E+00
mu is restricted to be zero
eta is restricted to be zero
iteration = 0 func evals = 20 llf = -0.40350876E+02
0.67575805E+01 0.80937646E-02 0.14644466E-02-0.59369192E-02 0.82661525E-03
0.52222441E-04 0.43649673E+00 0.79000000E+00
gradient step
iteration = 5 func evals = 45 llf = -0.40228729E+02
0.67576495E+01 0.84414672E-02 0.14193214E-02-0.55227894E-02 0.79163837E-03
0.50280731E-04 0.43643016E+00 0.79001464E+00
iteration = 10 func evals = 118 llf = -0.36681765E+02
0.80840545E+01 0.35466062E-02 0.56039148E-03-0.64908184E-02 0.52629048E-03
0.11217967E-04 0.67262457E+00 0.99999999E+00
iteration = 13 func evals = 166 llf = -0.36162931E+02
0.80616053E+01 0.35933673E-02 0.57209393E-03-0.64839078E-02 0.53007021E-03
0.12151838E-04 0.66625821E+00 0.99999999E+00
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the final mle estimates are :
coefficient
standard-error
t-ratio
beta 0
0.80616053E+01 0.24503604E+00 0.32899672E+02
beta 1
0.35933673E-02 0.24109221E-02 0.14904535E+01
beta 2
0.57209393E-03 0.45123288E-03 0.12678463E+01
beta 3
-0.64839078E-02 0.32263275E-02 -0.20096868E+01
beta 4
0.53007021E-03 0.19665115E-03 0.26954849E+01
beta 5
0.12151838E-04 0.27019405E-04 0.44974482E+00
sigma-squared 0.66625821E+00 0.78902234E-01 0.84440982E+01
gamma
0.99999999E+00 0.49395963E-05 0.20244569E+06
mu is restricted to be zero
eta is restricted to be zero
log likelihood function = -0.36162931E+02
LR test of the one-sided error = 0.12816266E+02
with number of restrictions = 1
[note that this statistic has a mixed chi-square distribution]
number of iterations =
13
(maximum number of iterations set at : 100)
number of cross-sections =
number of time periods =
65
1
total number of observations =
thus there are:
65
0 obsns not in the panel
covariance matrix :
0.60042660E-01 -0.24866093E-03 -0.71332645E-04
05
0.72483923E-06 0.14927887E-03 -0.91946638E-06
-0.24866093E-03 0.58125452E-05 -0.20188987E-07
07
-0.19013032E-07 0.16805755E-04 0.82609363E-08
-0.71332645E-04 -0.20188987E-07 0.20361111E-06
08
-0.25781037E-08 0.24349829E-06 0.36187008E-09
-0.23561933E-03 0.18087697E-05 -0.36675328E-06
07
0.54238413E-08 -0.40614317E-05 -0.50834173E-08
-0.82562075E-05 -0.93187500E-07 -0.87339269E-08
07
-0.17779245E-08 0.50763632E-06 0.29101141E-09
0.72483923E-06 -0.19013032E-07 -0.25781037E-08
08
-0.23561933E-03 -0.82562075E-
0.18087697E-05 -0.93187500E-
-0.36675328E-06 -0.87339269E-
0.10409189E-04 -0.34736208E-
-0.34736208E-07 0.38671676E-
0.54238413E-08 -0.17779245E-
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0.73004825E-09 0.19289444E-07 0.33650697E-10
0.14927887E-03 0.16805755E-04 0.24349829E-06 -0.40614317E-05 0.50763632E-06
0.19289444E-07 0.62255626E-02 -0.52335129E-07
-0.91946638E-06 0.82609363E-08 0.36187008E-09 -0.50834173E-08 0.29101141E09
0.33650697E-10 -0.52335129E-07 0.24399612E-10
technical efficiency estimates :
firm
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
eff.-est.
0.17655656E+00
0.99976365E+00
0.59315706E+00
0.37746358E+00
0.28689928E+00
0.64179517E+00
0.51677124E+00
0.44057477E+00
0.33961580E+00
0.72262972E+00
0.49275544E+00
0.45557126E+00
0.79309745E+00
0.85566742E+00
0.71128743E+00
0.86317682E+00
0.77619034E+00
0.43758173E+00
0.90601042E+00
0.83192607E+00
0.56982972E+00
0.57362692E-01
0.47705771E+00
0.42374829E+00
0.48853593E+00
0.44795986E+00
0.54754593E+00
0.88135466E+00
0.32767648E+00
0.44380964E+00
0.18398063E+00
0.13178763E+00
0.89520246E+00
0.77551802E+00
0.75655523E+00
0.28309939E+00
0.21355246E+00
0.25349668E+00
0.70207257E+00
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40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
0.55207011E+00
0.92011685E+00
0.36804674E+00
0.52139955E+00
0.49467517E+00
0.59921185E+00
0.43363587E+00
0.59767721E+00
0.88201839E+00
0.72583980E+00
0.72583980E+00
0.77591695E+00
0.55074862E+00
0.72662682E+00
0.45583457E+00
0.41523143E+00
0.65705232E+00
0.53385501E+00
0.75338624E+00
0.62782187E+00
0.82036816E+00
0.44417236E+00
0.92061128E+00
0.36159735E+00
0.34187210E+00
0.94150703E+00
mean efficiency = 0.57270416E+00
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Lampiran 16. Efisiensi Biaya
Output from the program FRONTIER (Version 4.1c)
instruction file = terminal
data file =
ce.txt
Error Components Frontier (see B&C 1992)
The model is a cost function
The dependent variable is not logged
the ols estimates are :
coefficient
standard-error
t-ratio
beta 0
0.84982766E+06 0.19760031E+07
beta 1
0.85484028E+02 0.10682378E+03
beta 2
0.15251250E+03 0.52017172E+02
beta 3
0.25140276E+02 0.21350122E+02
beta 4
0.12229446E+04 0.47802387E+03
beta 5
0.54036093E+04 0.29807234E+04
beta 6
0.60297779E+03 0.19797786E+03
sigma-squared 0.52023275E+13
0.43007406E+00
0.80023408E+00
0.29319644E+01
0.11775237E+01
0.25583338E+01
0.18128516E+01
0.30456830E+01
log likelihood function = -0.10401319E+04
the estimates after the grid search were :
beta 0
-0.14600961E+07
beta 1
0.85484028E+02
beta 2
0.15251250E+03
beta 3
0.25140276E+02
beta 4
0.12229446E+04
beta 5
0.54036093E+04
beta 6
0.60297779E+03
sigma-squared 0.99778247E+13
gamma
0.84000000E+00
mu is restricted to be zero
eta is restricted to be zero
iteration = 0 func evals = 20 llf = -0.10367505E+04
-0.14600961E+07
0.85484028E+02
0.15251250E+03
0.25140276E+02
0.12229446E+04
0.54036093E+04 0.60297779E+03 0.99778247E+13 0.84000000E+00
gradient step
iteration = 5 func evals = 120 llf = -0.10366821E+04
-0.14600961E+07
0.97229173E+02
0.15037538E+03
0.24937679E+02
0.11852554E+04
0.53874813E+04 0.63233013E+03 0.99778247E+13 0.84996483E+00
iteration = 10 func evals = 260 llf = -0.10366782E+04
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-0.14600961E+07
0.98073061E+02
0.14816355E+03
0.26200900E+02
0.11790952E+04
0.53848002E+04 0.63617845E+03 0.99778247E+13 0.85011294E+00
iteration = 15 func evals = 395 llf = -0.10366693E+04
-0.14600961E+07
0.90335621E+02
0.14974013E+03
0.26796400E+02
0.11604391E+04
0.53756618E+04 0.63972793E+03 0.99778247E+13 0.85159646E+00
iteration = 20 func evals = 547 llf = -0.10366587E+04
-0.14600961E+07
0.95886635E+02
0.15260680E+03
0.26569881E+02
0.11342212E+04
0.53572320E+04 0.62424997E+03 0.99778247E+13 0.85563719E+00
iteration = 25 func evals = 685 llf = -0.10366528E+04
-0.14600960E+07
0.97224952E+02
0.15281030E+03
0.27520236E+02
0.11187637E+04
0.53479663E+04 0.62248539E+03 0.99778247E+13 0.85035938E+00
iteration = 30 func evals = 836 llf = -0.10363934E+04
-0.14600931E+07
0.91566156E+02
0.16417275E+03
0.29254993E+02
0.11646589E+04
0.35826836E+04 0.59668812E+03 0.99778247E+13 0.86344123E+00
iteration = 35 func evals = 988 llf = -0.10363402E+04
-0.14600924E+07
0.89070688E+02
0.15866126E+03
0.30556098E+02
0.12060069E+04
0.31110263E+04 0.61154477E+03 0.99778247E+13 0.87494649E+00
iteration = 40 func evals = 1151 llf = -0.10363286E+04
-0.14600923E+07
0.84758378E+02
0.15897780E+03
0.30423091E+02
0.12079365E+04
0.30739129E+04 0.61287385E+03 0.99778247E+13 0.87228647E+00
iteration = 45 func evals = 1264 llf = -0.10363277E+04
-0.14600924E+07
0.85822324E+02
0.15879742E+03
0.30478636E+02
0.12047371E+04
0.31292578E+04 0.61267234E+03 0.99778247E+13 0.86981695E+00
the final mle estimates are :
coefficient
standard-error
t-ratio
beta 0
-0.14600924E+07 0.46375390E+01 -0.31484207E+06
beta 1
0.85822324E+02 0.84770777E+02 0.10124046E+01
beta 2
0.15879742E+03 0.33090494E+02 0.47988834E+01
beta 3
0.30478636E+02 0.15777989E+02 0.19317187E+01
beta 4
0.12047371E+04 0.32862507E+03 0.36659927E+01
beta 5
0.31292578E+04 0.27578500E+04 0.11346729E+01
beta 6
0.61267234E+03 0.16316096E+03 0.37550180E+01
sigma-squared 0.99778247E+13 0.10000000E+01 0.99778247E+13
gamma
0.86981695E+00 0.63007507E-01 0.13804973E+02
mu is restricted to be zero
eta is restricted to be zero
log likelihood function = -0.10363277E+04
LR test of the one-sided error = 0.76084208E+01
with number of restrictions = 1
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[note that this statistic has a mixed chi-square distribution]
number of iterations =
45
(maximum number of iterations set at : 100)
number of cross-sections =
number of time periods =
65
1
total number of observations =
thus there are:
65
0 obsns not in the panel
covariance matrix :
0.21506768E+02
-0.44584335E+02
0.33103296E+02
0.18394994E+02
0.37585974E+03
-0.12487494E+05 -0.52212146E+02 -0.60171505E-10 0.10496521E+00
-0.44584335E+02
0.71860846E+04 -0.82987176E+03 -0.20196428E+03 0.10388798E+05
0.25986762E+05 0.16738445E+04 0.83109372E-09 -0.93574651E+00
0.33103296E+02 -0.82987176E+03
0.10949808E+04 -0.11979260E+03 0.10146452E+04
-0.20290069E+05 -0.35936661E+04 -0.22753638E-09 0.46708366E-01
0.18394994E+02 -0.20196428E+03 -0.11979260E+03
0.24894494E+03 0.14463354E+04
-0.11420432E+05 -0.16457771E+03 0.19679609E-09 -0.16383508E-01
0.37585974E+03
-0.10388798E+05
-0.10146452E+04
-0.14463354E+04
0.10799444E+06
-0.21643308E+06 -0.69668557E+04 -0.20247160E-08 0.16072777E+01
-0.12487494E+05
0.25986762E+05 -0.20290069E+05 -0.11420432E+05 0.21643308E+06
0.76057366E+07 0.30592030E+05 0.36172787E-07 -0.63983041E+02
-0.52212146E+02
0.16738445E+04 -0.35936661E+04 -0.16457771E+03 0.69668557E+04
0.30592030E+05 0.26621499E+05 -0.96898697E-09 0.11160811E+01
-0.60171505E-10 0.83109372E-09 -0.22753638E-09 0.19679609E-09 -0.20247160E08
0.36172787E-07 -0.96898697E-09 0.10000000E+01 0.12629558E-11
0.10496521E+00
-0.93574651E+00
0.46708366E-01
-0.16383508E-01
0.16072777E+01
-0.63983041E+02 0.11160811E+01 0.12629558E-11 0.39699460E-02
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cost efficiency estimates :
firm
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
eff.-est.
0.16053292E+01
0.11311818E+01
0.15170463E+01
0.17753817E+01
0.12029803E+01
0.11097879E+01
0.12669209E+01
0.12352208E+01
0.13184070E+01
0.11440727E+01
0.12078802E+01
0.12452644E+01
0.11638045E+01
0.11108180E+01
0.10569729E+01
0.10657499E+01
0.11023566E+01
0.11564917E+01
0.10791273E+01
0.11974075E+01
0.12390049E+01
0.16238271E+01
0.12910152E+01
0.12029333E+01
0.12332375E+01
0.11701268E+01
0.12180568E+01
0.10806149E+01
0.16848811E+01
0.11445650E+01
0.12288881E+01
0.12284044E+01
0.10476250E+01
0.11818015E+01
0.11844483E+01
0.13031708E+01
0.12044030E+01
0.12924264E+01
0.11114633E+01
0.11922703E+01
0.10780313E+01
0.12890885E+01
0.12033253E+01
0.11810217E+01
0.10797192E+01
0.14564265E+01
0.15266230E+01
0.11278178E+01
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49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
0.12182411E+01
0.12166252E+01
0.14514238E+01
0.11181698E+01
0.11082843E+01
0.10469225E+01
0.10432699E+01
0.11439679E+01
0.11569011E+01
0.12480089E+01
0.12995103E+01
0.10558574E+01
0.12971666E+01
0.10456274E+01
0.12002279E+01
0.13006443E+01
0.12021132E+01
mean efficiency = 0.12253905E+01
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Lampiran 17. Hasil Regresi Faktor-Faktor yang Mempengaruhi Efisiensi
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT te
/METHOD=ENTER z1 z2 z3 z4 z5
/SCATTERPLOT=(*ZPRED ,*SRESID )
/RESIDUALS DURBIN HIST(ZRESID) NORM(ZRESID)
/SAVE RESID .
Regression
[DataSet0]
Descriptive Statistics
ef isiensi teknis
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
Mean
,5760
9,6000
17,5077
77,4308
St d. Dev iation
,21859
2,89828
13,66465
38,43662
N
2,1385
1,28546
65
,3846
,49029
65
65
65
65
65
Correlati ons
Pearson Correlation
Sig. (1-tailed)
N
ef isiensi teknis
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
ef isiensi teknis
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
ef isiensi teknis
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
ef isiensi
teknis
1,000
-,288
,061
-,069
tingkat
pendidikan
-,288
1,000
-,029
,277
pengalaman
bertani
,061
-,029
1,000
,190
ketersediaan
modal
-,069
,277
,190
1,000
jumlah lahan
y ang
diusahakan
-,140
-,035
-,180
-,336
serangan
hama
-,681
,297
,099
,358
-,140
-,035
-,180
-,336
1,000
-,160
-,681
.
,010
,316
,291
,297
,010
.
,409
,013
,099
,316
,409
.
,064
,358
,291
,013
,064
.
-,160
,133
,390
,075
,003
1,000
,000
,008
,217
,002
,133
,390
,075
,003
.
,101
,000
65
65
65
65
,008
65
65
65
65
,217
65
65
65
65
,002
65
65
65
65
,101
65
65
65
65
.
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
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Variabl es Entered/Removedb
Model
1
Variables
Entered
serangan
hama,
pengalam
an bertani,
jumlah
lahan y ang
diusahaka
n, tingkat
pendidika
n,
ketersediaa
an modal
Variables
Remov ed
Method
.
Enter
a. All requested v ariables entered.
b. Dependent Variable: ef isiensi teknis
Model Summaryb
Model
1
R
,747a
R Square
,557
Adjusted
R Square
,520
St d. Error of
the Estimate
,15146
DurbinWat son
2,068
a. Predictors: (Constant), serangan hama, pengalaman bertani, jumlah
lahan y ang diusahakan, tingkat pendidikan, ketersediaan modal
b. Dependent Variable: ef isiensi teknis
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
1,704
1,354
3,058
df
5
59
64
Mean Square
,341
,023
F
14,859
Sig.
,000a
a. Predictors: (Const ant), serangan hama, pengalaman bertani, jumlah lahan y ang
diusahakan, tingkat pendidikan, ketersediaan modal
b. Dependent Variable: ef isiensi teknis
Coeffi ci entsa
Model
1
(Constant)
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
Unstandardized
Coef f icients
B
St d. Error
,776
,087
-,009
,007
,001
,001
,001
,001
St andardized
Coef f icients
Beta
Correlations
Part ial
Part
Collinearity Statistics
Tolerance
VI F
Sig.
,000
,221
,463
,151
Zero-order
-,115
,066
,146
t
8,954
-1,237
,739
1,455
-,288
,061
-,069
-,159
,096
,186
-,107
,064
,126
,866
,940
,744
1,154
1,063
1,344
-,034
,016
-,201
-2,161
,035
-,140
-,271
-,187
,868
1,153
-,329
,043
-,738
-7,735
,000
-,681
-,710
-,670
,825
1,212
a. Dependent Variable: ef isiensi teknis
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a
Colli neari ty Diagnostics
Variance Proportions
Model
1
Dimension
1
2
3
4
5
6
Eigenv alue
4,710
,578
,379
,216
,084
,033
Condit ion
Index
1,000
2,855
3,525
4,674
7,497
11,934
tingkat
pendidikan
,00
,00
,00
,02
,36
,61
(Constant)
,00
,00
,00
,00
,04
,95
pengalaman
bertani
,01
,00
,61
,30
,00
,08
ketersediaan
modal
,01
,00
,01
,27
,66
,06
jumlah lahan
y ang
diusahakan
,01
,09
,15
,24
,34
,18
serangan
hama
,01
,63
,09
,23
,00
,03
a. Dependent Variable: ef isiensi teknis
Residual s Stati sticsa
Minimum
,2471
-2,015
Predicted Value
St d. Predicted Value
St andard Error of
Predicted Value
Adjusted Predict ed Value
Residual
St d. Residual
St ud. Residual
Delet ed Residual
St ud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Lev erage Value
Maximum
,7944
1,338
Mean
,5760
,000
St d. Dev iation
,16319
1,000
N
65
65
,033
,064
,045
,008
65
,2049
-,35220
-2,325
-2,382
-,37412
-2,485
2,038
,000
,032
,8141
,36748
2,426
2,576
,41965
2,711
10,496
,174
,164
,5759
,00000
,000
,000
,00014
,001
4,923
,017
,077
,16306
,14543
,960
1,007
,16004
1,027
2,086
,027
,033
65
65
65
65
65
65
65
65
65
a. Dependent Variable: ef isiensi teknis
Charts
Histogram
Dependent Variable: efisiensi teknis
12.5
Frequency
10.0
7.5
5.0
2.5
Mean =1.4
Std. Dev.
N =6
0.0
-3
-2
-1
0
1
2
3
Regression Standardized Residual
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Normal P-P Plot of Regression Standardized Residual
Dependent Variable: efisiensi teknis
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Scatterplot
Regression Standardized Predicted
Value
Dependent Variable: efisiensi teknis
1
0
-1
-2
-3
-2
-1
0
1
2
Regression Studentized Residual
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Lampiran 7. Uji Linearitas Ramsey Reset Test Persamaan Produksi
msey RESET Test: Data Level
statistic
g likelihood ratio
1.917704
Prob. F(2,57)
4.232846
Prob. Chi-Square(2)
0.1563
0.1205
st Equation:
pendent Variable: Y
Method: Least Squares
te: 08/19/15 Time: 08:19
mple: 1 65
luded observations: 65
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
X1
X2
X3
X4
X5
FITTED^2
FITTED^3
9937.751
-93.71989
-12.40053
72.80448
-9.293042
-0.776462
0.001233
-9.79E-08
5971.710
62.56536
8.239320
48.87239
6.227232
0.512677
0.000631
5.01E-08
1.664138
-1.497952
-1.505042
1.489685
-1.492323
-1.514524
1.955263
-1.954356
0.1016
0.1397
0.1378
0.1418
0.1411
0.1354
0.0555
0.0556
squared
Adjusted R-squared
.E. of regression
um squared resid
g likelihood
statistic
rob(F-statistic)
0.521944
Mean dependent var
0.463235
S.D. dependent var
1386.414
Akaike info criterion
1.10E+08
Schwarz criterion
-558.2035
Hannan-Quinn criter.
8.890416
Durbin-Watson stat
0.000000
3997.769
1892.349
17.42165
17.68926
17.52724
2.044358
Nilai signifikansi fittet^2 0,050,1 maka H0 : model linear tidak dapat
ditolak.
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Lampiran 9.
Uji Linearitas Ramsey
Mempengaruhi Efisiensi
ReseT
Test
Faktor-Faktor
yang
msey RESET Test:
statistic
g likelihood ratio
1.798786
Prob. F(2,57)
3.978231
Prob. Chi-Square(2)
0.1748
0.1368
st Equation:
pendent Variable: TE
Method: Least Squares
te: 08/19/15 Time: 09:27
mple: 1 65
luded observations: 65
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
Z1
Z2
Z3
Z4
Z5
FITTED^2
FITTED^3
-0.022835
0.000432
-0.000337
0.001070
-0.012628
-0.076315
4.255372
-3.968744
3.918433
0.007465
0.002556
0.011556
0.265619
2.830830
14.16755
8.442903
-0.005828
0.057890
-0.131686
0.092576
-0.047543
-0.026958
0.300361
-0.470069
0.9954
0.9540
0.8957
0.9266
0.9622
0.9786
0.7650
0.6401
squared
Adjusted R-squared
.E. of regression
um squared resid
g likelihood
statistic
rob(F-statistic)
0.601918
Mean dependent var
0.553031
S.D. dependent var
0.152469
Akaike info criterion
1.325067
Schwarz criterion
34.28902
Hannan-Quinn criter.
12.31239
Durbin-Watson stat
0.000000
0.586449
0.228057
-0.808893
-0.541276
-0.703301
1.727892
Nilai fitted^2 adalah 0,7>0,1 maka H0 : model linear tidak dapat ditolak.
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Lampiran 10. Uji Normalitas Kolmogorof-Smirnov Residual Data Produksi
Regression
[DataSet0]
Variabl es Entered/Removedb
Model
1
Variables
Entered
lnx5, lnx1,
lnx2,a lnx4,
lnx3
Variables
Remov ed
Method
.
Enter
a. All requested v ariables entered.
b. Dependent Variable: lny
Model Summaryb
Model
1
R
,684a
R Square
,468
Adjusted
R Square
,423
St d. Error of
the Estimate
,48494
a. Predictors: (Constant), lnx5, lnx1, lnx2, lnx4, lnx3
b. Dependent Variable: lny
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
12,213
13,875
26,088
df
5
59
64
Mean Square
2,443
,235
F
10,387
Sig.
,000a
t
-1,202
1,814
3,516
-1,649
3,861
1,692
Sig.
,234
,075
,001
,104
,000
,096
a. Predictors: (Const ant), lnx5, lnx1, lnx2, lnx4, lnx3
b. Dependent Variable: lny
Coeffi ci entsa
Model
1
(Constant)
lnx1
lnx2
lnx3
lnx4
lnx5
Unstandardized
Coef f icients
B
St d. Error
-1,826
1,520
,345
,190
,743
,211
-,208
,126
,477
,124
,193
,114
St andardized
Coef f icients
Beta
,180
,356
-,172
,397
,186
a. Dependent Variable: lny
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Residual s Statisti csa
Predicted Value
Residual
Std. Predicted Value
Std. Residual
Minimum
6,9772
-1,66563
-2,660
-3,435
Maximum
8,8385
1,11625
1,601
2,302
Mean
8,1393
,00000
,000
,000
Std. Dev iat ion
,43684
,46561
1,000
,960
N
65
65
65
65
a. Dependent Variable: lny
NPAR TESTS
/K-S(NORMAL)= RES_1
/MISSING ANALYSIS.
NPar Tests
[DataSet0]
One-Sample Kolmogorov-Smirnov Test
N
Normal Parameters a,b
Most Extreme
Dif f erences
Mean
Std. Dev iat ion
Absolute
Positiv e
Negativ e
Kolmogorov -Smirnov Z
Asy mp. Sig. (2-tailed)
Unstandardiz
ed Residual
65
,0000000
,46560869
,090
,064
-,090
,723
,673
a. Test distribution is Normal.
b. Calculated f rom data.
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Lampiran 11. Uji Normalitas Kolmogorof-Smirnov Residual Data Biaya Produksi
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT C
/METHOD=ENTER PX1 PX2 PX3 PX4 PX5 Y
/SAVE RESID .
Regression
[DataSet0]
Variabl es Entered/Removedb
Model
1
Variables
Entered
Produksi,
Harga
pupuk,
Harga
bibit , Sewa
traktor,
Harga
pestisida,
Upah
tenaga
a
kerja
Variables
Remov ed
.
Method
Enter
a. All requested v ariables entered.
b. Dependent Variable: Biay a produksi
Model Summaryb
Model
1
R
,738a
R Square
,545
Adjusted
R Square
,498
St d. Error of
the Estimate
2280861,12
a. Predictors: (Constant), Produksi, Harga pupuk, Harga
bibit , Sewa traktor, Harga pestisida, Upah t enaga kerja
b. Dependent Variable: Biay a produksi
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
3,6E+014
3,0E+014
6,6E+014
df
6
58
64
Mean Square
6,024E+013
5,202E+012
F
11,579
Sig.
,000a
a. Predictors: (Const ant), Produksi, Harga pupuk, Harga bibit, Sewa trakt or, Harga
pestisida, Upah tenaga kerja
b. Dependent Variable: Biay a produksi
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Coeffi cientsa
Model
1
(Constant)
Harga bibit
Upah tenaga kerja
Sewa traktor
Harga pupuk
Harga pestisida
Produksi
Unstandardized
Coef f icients
B
St d. Error
849827,7
1976003
85,484
106,824
152,512
52,017
25,140
21,350
1222,945
478,024
5403,609
2980,723
602,978
197,978
St andardized
Coef f icients
Beta
t
,430
,800
2,932
1,178
2,558
1,813
3,046
,075
,346
,108
,236
,170
,354
Sig.
,669
,427
,005
,244
,013
,075
,003
a. Dependent Variable: Biay a produksi
Residual s Statisti csa
Predicted Value
Residual
Std. Predicted Value
Std. Residual
Minimum
7830103
-4438223
-2,222
-1,946
Maximum
2E+007
8795421
1,982
3,856
Mean
1E+007
,00000
,000
,000
Std. Dev iat ion
2376447,147
2171315,101
1,000
,952
N
65
65
65
65
a. Dependent Variable: Biay a produksi
NPAR TESTS
/K-S(NORMAL)= RES_1
/MISSING ANALYSIS.
NPar Tests
[DataSet0]
One-Sample Kolmogorov-Smirnov Test
N
Normal Parameters a,b
Most Extreme
Dif f erences
Mean
Std. Dev iat ion
Absolute
Positiv e
Negativ e
Kolmogorov -Smirnov Z
Asy mp. Sig. (2-tailed)
Unstandardiz
ed Residual
65
,0000000
2171315,101
,128
,128
-,079
1,030
,239
a. Test distribution is Normal.
b. Calculated f rom data.
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Lampiran 12. Uji Normalitas Kolmogorof-Smirnov Residual Faktor-Faktor yang
Mempengaruhi Efisiensi
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT te
/METHOD=ENTER z1 z2 z3 z4 z5
/SAVE RESID .
Regression
[DataSet0]
Variabl es Entered/Removedb
Model
1
Variables
Entered
serangan
hama,
pengalam
an bertani,
jumlah
lahan y ang
diusahaka
n, tingkat
pendidika
n,
ketersediaa
an modal
Variables
Remov ed
Method
.
Enter
a. All requested v ariables entered.
b. Dependent Variable: ef isiensi teknis
Model Summaryb
Model
1
R
,734a
R Square
,539
Adjusted
R Square
,500
St d. Error of
the Estimate
,15699
a. Predictors: (Constant), serangan hama, pengalaman
bertani, jumlah lahan y ang diusahakan, tingkat
pendidikan, ketersediaan modal
b. Dependent Variable: ef isiensi teknis
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
1,701
1,454
3,155
df
5
59
64
Mean Square
,340
,025
F
13,806
Sig.
,000a
a. Predictors: (Const ant), serangan hama, pengalaman bertani, jumlah lahan y ang
diusahakan, tingkat pendidikan, ketersediaan modal
b. Dependent Variable: ef isiensi teknis
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Coeffici entsa
Model
1
(Constant)
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
Unstandardized
Coef f icients
B
Std. Error
,674
,090
,002
,007
,001
,001
,001
,001
Standardized
Coef f icients
Beta
,026
,042
,248
t
7,504
,274
,456
2,422
Sig.
,000
,785
,650
,019
-,042
,016
-,245
-2,580
,012
-,343
,044
-,758
-7,791
,000
a. Dependent Variable: ef isiensi t eknis
Residual s Statisti csa
Predicted Value
Residual
Std. Predicted Value
Std. Residual
Minimum
,3395
-,30384
-1,558
-1,935
Maximum
,8234
,40674
1,410
2,591
Mean
,5935
,00000
,000
,000
Std. Dev iat ion
,16304
,15073
1,000
,960
N
65
65
65
65
a. Dependent Variable: ef isiensi t eknis
NPAR TESTS
/K-S(NORMAL)= RES_1
/MISSING ANALYSIS.
NPar Tests
[DataSet0]
One-Sample Kolmogorov-Smirnov Test
N
Normal Parameters a,b
Most Extrem e
Dif f erences
Mean
Std. Dev iat ion
Absolute
Positiv e
Negativ e
Kolmogorov -Smirnov Z
Asy mp. Sig. (2-tailed)
Unstandardiz
ed Residual
65
,0000000
,15073485
,069
,069
-,058
,559
,914
a. Test distribution is Normal.
b. Calculated f rom data.
Universitas Sumatera Utara
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Lampiran 13. Hasil Analisis Regresi Persamaan Produksi
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT lny
/METHOD=ENTER x1 x2 x3 x4 x5
/SCATTERPLOT=(*ZPRED ,*SRESID )
/RESIDUALS DURBIN HIST(ZRESID) NORM(ZRESID)
/SAVE RESID .
Regression
[DataSet0]
Descriptive Statistics
produksi
bibit
tenaga kerja
traktor
pupuk
pestisida
Mean
8,1393
62,3692
499,0923
29,2923
706,5077
3915,6154
St d. Dev iation
,63845
19,63620
130,12034
18,56048
307,99204
2154,34878
N
65
65
65
65
65
65
Correlati ons
Pearson Correlation
Sig. (1-tailed)
N
produksi
bibit
tenaga kerja
traktor
pupuk
pestisida
produksi
bibit
tenaga kerja
traktor
pupuk
pestisida
produksi
bibit
tenaga kerja
traktor
pupuk
pestisida
produksi
1,000
,330
,355
-,064
,486
,379
.
,004
,002
,307
,000
,001
65
65
65
65
65
65
bibit
,330
1,000
,021
-,099
,067
,173
,004
.
,434
,216
,297
,084
65
65
65
65
65
65
tenaga kerja
,355
,021
1,000
,221
,126
,221
,002
,434
.
,039
,159
,039
65
65
65
65
65
65
traktor
-,064
-,099
,221
1,000
,110
,135
,307
,216
,039
.
,191
,141
65
65
65
65
65
65
pupuk
,486
,067
,126
,110
1,000
,294
,000
,297
,159
,191
.
,009
65
65
65
65
65
65
pestisida
,379
,173
,221
,135
,294
1,000
,001
,084
,039
,141
,009
.
65
65
65
65
65
65
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Variabl es Entered/Removedb
Model
1
Variables
Entered
pestisida,
traktor,
bibit ,
tenaga
kerja, a
pupuk
Variables
Remov ed
Method
.
Enter
a. All requested v ariables entered.
b. Dependent Variable: produksi
Model Summaryb
Model
1
R
,678a
Adjusted
R Square
,414
R Square
,459
St d. Error of
the Estimate
,48891
DurbinWat son
2,015
a. Predictors: (Constant), pestisida, trakt or, bibit, tenaga kerja, pupuk
b. Dependent Variable: produksi
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
11,985
14,103
26,088
df
Mean Square
2,397
,239
5
59
64
F
10,028
Sig.
,000a
a. Predictors: (Const ant), pestisida, traktor, bibit, tenaga kerja, pupuk
b. Dependent Variable: produksi
Coeffi ci entsa
Model
1
(Constant)
bibit
tenaga kerja
traktor
pupuk
pestisida
Unstandardized
Coef f icients
B
St d. Error
6,289
,328
,008
,003
,001
,000
-,006
,003
,001
,000
5,22E-005
,000
St andardized
Coef f icients
Beta
,249
,298
-,173
,399
,176
t
19,170
2,540
2,977
-1,734
3,963
1,697
Sig.
,000
,014
,004
,088
,000
,095
Zero-order
Correlations
Part ial
,330
,355
-,064
,486
,379
,314
,361
-,220
,459
,216
Part
,243
,285
-,166
,379
,162
Collinearity Statistics
Tolerance
VI F
,954
,912
,925
,905
,849
1,048
1,097
1,081
1,105
1,177
a. Dependent Variable: produksi
a
Colli neari ty Diagnostics
Model
1
Dimension
1
2
3
4
5
6
Eigenv alue
5,370
,251
,164
,123
,067
,024
Condit ion
Index
1,000
4,626
5,717
6,613
8,939
14,878
(Constant)
,00
,00
,02
,00
,01
,96
bibit
,00
,02
,07
,09
,51
,30
Variance Proportions
tenaga kerja
traktor
,00
,01
,00
,84
,02
,02
,01
,00
,45
,12
,52
,01
pupuk
,00
,02
,00
,90
,03
,05
pestisida
,01
,06
,85
,07
,00
,01
a. Dependent Variable: produksi
Universitas Sumatera Utara
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Residual s Stati sticsa
Predicted Value
St d. Predicted Value
St andard Error of
Predicted Value
Adjusted Predict ed Value
Residual
St d. Residual
St ud. Residual
Delet ed Residual
St ud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Lev erage Value
Minimum
7,2313
-2,098
Maximum
9,1066
2,235
Mean
8,1393
,000
St d. Dev iation
,43274
1,000
N
,065
,246
,144
,038
65
7,1445
-1,83926
-3,762
-4,126
-2,21246
-4,850
,163
,000
,003
9,2334
1,08847
2,226
2,347
1,21013
2,444
15,188
,576
,237
8,1457
,00000
,000
-,006
-,00634
-,018
4,923
,022
,077
,43539
,46942
,960
1,018
,52817
1,078
3,302
,073
,052
65
65
65
65
65
65
65
65
65
65
65
a. Dependent Variable: produksi
Charts
Histogram
Dependent Variable: produksi
25
Frequency
20
15
10
5
Mean =-6.8
Std. Dev.
N =6
0
-4
-2
0
2
Regression Standardized Residual
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Normal P-P Plot of Regression Standardized Residual
Dependent Variable: produksi
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Scatterplot
Dependent Variable: produksi
Regression Standardized Predicted
Value
3
2
1
0
-1
-2
-3
-4
-2
0
2
Regression Studentized Residual
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Lampiran 14. Regresi Biaya Produksi
[DataSet0]
Descriptive Statistics
Mean
1E+007
6727,1692
29268,12
44799,49
2165,3538
192,0308
3997,7692
biay a produksi
harga bibit
upah tenaga kerja
sewa t raktor
harga pupuk
harga pestisida
produksi
St d. Dev iation
3219023,192
2806,61996
7296,13403
13826,79671
621,96120
101,13774
1892,34860
N
65
65
65
65
65
65
65
Correlati ons
Pearson Correlation
Sig. (1-tailed)
N
biay a produksi
harga bibit
upah tenaga kerja
sewa t raktor
harga pupuk
harga pestisida
produksi
biay a produksi
harga bibit
upah tenaga kerja
sewa t raktor
harga pupuk
harga pestisida
produksi
biay a produksi
harga bibit
upah tenaga kerja
sewa t raktor
harga pupuk
harga pestisida
produksi
biay a
produksi
1,000
-,077
,570
,261
,257
,312
,597
.
,272
,000
,018
,019
,006
,000
65
65
65
65
65
65
65
harga bibit
-,077
1,000
-,233
,043
,088
-,191
-,180
,272
.
,031
,368
,244
,064
,076
65
65
65
65
65
65
65
upah tenaga
kerja
,570
-,233
1,000
,090
-,092
,165
,638
,000
,031
.
,239
,232
,095
,000
65
65
65
65
65
65
65
sewa t raktor
,261
,043
,090
1,000
,165
,140
,158
,018
,368
,239
.
,094
,132
,105
65
65
65
65
65
65
65
harga pupuk
,257
,088
-,092
,165
1,000
,180
-,007
,019
,244
,232
,094
.
,076
,479
65
65
65
65
65
65
65
harga
pestisida
,312
-,191
,165
,140
,180
1,000
,118
,006
,064
,095
,132
,076
.
,175
65
65
65
65
65
65
65
produksi
,597
-,180
,638
,158
-,007
,118
1,000
,000
,076
,000
,105
,479
,175
.
65
65
65
65
65
65
65
Variabl es Entered/Removedb
Model
1
Variables
Entered
produksi,
harga
pupuk,
harga bibit,
sewa
traktor,
harga
pestisida,
upah
tenaga
a
kerja
Variables
Remov ed
.
Method
Enter
a. All requested v ariables entered.
b. Dependent Variable: biay a produksi
Universitas Sumatera Utara
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Model Summaryb
Model
1
R
,738a
Adjusted
R Square
,498
R Square
,545
St d. Error of
the Estimate
2280861,12
DurbinWat son
1,726
a. Predictors: (Constant), produksi, harga pupuk, harga bibit , sewa t raktor,
harga pestisida, upah tenaga kerja
b. Dependent Variable: biay a produksi
ANOVAb
Model
1
Sum of
Squares
3,6E+014
3,0E+014
6,6E+014
Regression
Residual
Total
df
6
58
64
Mean Square
6,024E+013
5,202E+012
F
11,579
Sig.
,000a
a. Predictors: (Const ant), produksi, harga pupuk, harga bibit, sewa traktor, harga
pestisida, upah tenaga kerja
b. Dependent Variable: biay a produksi
Coefficientsa
Model
1
(Constant)
harga bibit
upah tenaga kerja
sewa traktor
harga pupuk
harga pestisida
produksi
Unstandardized
Coeff icients
B
Std. Error
849827,7
1976003
85,484
106,824
152,512
52,017
25,140
21,350
1222,945
478,024
5403,609
2980,723
602,978
197,978
Standardized
Coeff icients
Beta
t
,075
,346
,108
,236
,170
,354
,430
,800
2,932
1,178
2,558
1,813
3,046
Sig.
,669
,427
,005
,244
,013
,075
,003
Zero-order
Correlations
Partial
-,077
,570
,261
,257
,312
,597
,105
,359
,153
,318
,232
,371
Collinearity Statistics
Tolerance
VIF
Part
,071
,260
,104
,227
,161
,270
,904
,564
,933
,920
,894
,579
1,106
1,772
1,072
1,087
1,118
1,727
a. Dependent Variable: biay a produksi
a
Collinearity Diagnostics
Model
1
Dimension
1
2
3
4
5
6
7
Eigenvalue
6,375
,228
,186
,085
,065
,045
,014
Condition
Index
1,000
5,286
5,849
8,646
9,868
11,902
21,078
(Constant)
,00
,00
,00
,00
,00
,08
,91
harga bibit
,00
,29
,01
,47
,03
,07
,14
Variance Proportions
upah tenaga
sewa traktor harga pupuk
kerja
,00
,00
,00
,00
,00
,01
,01
,00
,01
,00
,33
,14
,00
,55
,54
,34
,06
,13
,64
,05
,17
harga
pestisida
,00
,25
,47
,22
,03
,03
,00
produksi
,00
,06
,25
,08
,00
,47
,13
a. Dependent Variable: biaya produksi
Universitas Sumatera Utara
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Residual s Stati sticsa
Predicted Value
St d. Predicted Value
St andard Error of
Predicted Value
Adjusted Predict ed Value
Residual
St d. Residual
St ud. Residual
Delet ed Residual
St ud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Lev erage Value
Minimum
7830103
-2,222
Maximum
2E+007
1,982
Mean
1E+007
,000
St d. Dev iation
2376447,147
1,000
N
325461,9
1264945
719634,4
207458,554
65
8172192
-4438223
-1,946
-2,063
-4988842
-2,125
,318
,000
,005
2E+007
8795421
3,856
3,977
9355220
4,623
18,700
,144
,292
1E+007
,00000
,000
-,009
-44706,7
,003
5,908
,016
,092
2405054,439
2171315,101
,952
1,003
2412998,785
1,059
3,978
,028
,062
65
65
65
65
65
65
65
65
65
65
65
a. Dependent Variable: biay a produksi
Charts
Histogram
Dependent Variable: biaya produksi
20
Frequency
15
10
5
Mean =-2.2
Std. Dev. =
N =6
0
-2
-1
0
1
2
3
4
Regression Standardized Residual
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Normal P-P Plot of Regression Standardized Residual
Dependent Variable: biaya produksi
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Scatterplot
Dependent Variable: biaya produksi
Regression Standardized Predicted
Value
2
1
0
-1
-2
-3
-2
0
2
Regression Studentized Residual
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Lampiran 15. Efisiensi Teknis
Output from the program FRONTIER (Version 4.1c)
instruction file = terminal
data file =
logln.txt
Error Components Frontier (see B&C 1992)
The model is a production function
The dependent variable is logged
the ols estimates are :
coefficient
standard-error
t-ratio
beta 0
0.62890432E+01 0.32806483E+00 0.19170123E+02
beta 1
0.80937646E-02 0.31863093E-02 0.25401692E+01
beta 2
0.14644466E-02 0.49188918E-03 0.29771881E+01
beta 3
-0.59369192E-02 0.34242270E-02 -0.17337984E+01
beta 4
0.82661525E-03 0.20855789E-03 0.39634812E+01
beta 5
0.52222441E-04 0.30780392E-04 0.16966139E+01
sigma-squared 0.23903429E+00
log likelihood function = -0.42571065E+02
the estimates after the grid search were :
beta 0
0.67575805E+01
beta 1
0.80937646E-02
beta 2
0.14644466E-02
beta 3
-0.59369192E-02
beta 4
0.82661525E-03
beta 5
0.52222441E-04
sigma-squared 0.43649673E+00
gamma
0.79000000E+00
mu is restricted to be zero
eta is restricted to be zero
iteration = 0 func evals = 20 llf = -0.40350876E+02
0.67575805E+01 0.80937646E-02 0.14644466E-02-0.59369192E-02 0.82661525E-03
0.52222441E-04 0.43649673E+00 0.79000000E+00
gradient step
iteration = 5 func evals = 45 llf = -0.40228729E+02
0.67576495E+01 0.84414672E-02 0.14193214E-02-0.55227894E-02 0.79163837E-03
0.50280731E-04 0.43643016E+00 0.79001464E+00
iteration = 10 func evals = 118 llf = -0.36681765E+02
0.80840545E+01 0.35466062E-02 0.56039148E-03-0.64908184E-02 0.52629048E-03
0.11217967E-04 0.67262457E+00 0.99999999E+00
iteration = 13 func evals = 166 llf = -0.36162931E+02
0.80616053E+01 0.35933673E-02 0.57209393E-03-0.64839078E-02 0.53007021E-03
0.12151838E-04 0.66625821E+00 0.99999999E+00
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the final mle estimates are :
coefficient
standard-error
t-ratio
beta 0
0.80616053E+01 0.24503604E+00 0.32899672E+02
beta 1
0.35933673E-02 0.24109221E-02 0.14904535E+01
beta 2
0.57209393E-03 0.45123288E-03 0.12678463E+01
beta 3
-0.64839078E-02 0.32263275E-02 -0.20096868E+01
beta 4
0.53007021E-03 0.19665115E-03 0.26954849E+01
beta 5
0.12151838E-04 0.27019405E-04 0.44974482E+00
sigma-squared 0.66625821E+00 0.78902234E-01 0.84440982E+01
gamma
0.99999999E+00 0.49395963E-05 0.20244569E+06
mu is restricted to be zero
eta is restricted to be zero
log likelihood function = -0.36162931E+02
LR test of the one-sided error = 0.12816266E+02
with number of restrictions = 1
[note that this statistic has a mixed chi-square distribution]
number of iterations =
13
(maximum number of iterations set at : 100)
number of cross-sections =
number of time periods =
65
1
total number of observations =
thus there are:
65
0 obsns not in the panel
covariance matrix :
0.60042660E-01 -0.24866093E-03 -0.71332645E-04
05
0.72483923E-06 0.14927887E-03 -0.91946638E-06
-0.24866093E-03 0.58125452E-05 -0.20188987E-07
07
-0.19013032E-07 0.16805755E-04 0.82609363E-08
-0.71332645E-04 -0.20188987E-07 0.20361111E-06
08
-0.25781037E-08 0.24349829E-06 0.36187008E-09
-0.23561933E-03 0.18087697E-05 -0.36675328E-06
07
0.54238413E-08 -0.40614317E-05 -0.50834173E-08
-0.82562075E-05 -0.93187500E-07 -0.87339269E-08
07
-0.17779245E-08 0.50763632E-06 0.29101141E-09
0.72483923E-06 -0.19013032E-07 -0.25781037E-08
08
-0.23561933E-03 -0.82562075E-
0.18087697E-05 -0.93187500E-
-0.36675328E-06 -0.87339269E-
0.10409189E-04 -0.34736208E-
-0.34736208E-07 0.38671676E-
0.54238413E-08 -0.17779245E-
Universitas Sumatera Utara
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0.73004825E-09 0.19289444E-07 0.33650697E-10
0.14927887E-03 0.16805755E-04 0.24349829E-06 -0.40614317E-05 0.50763632E-06
0.19289444E-07 0.62255626E-02 -0.52335129E-07
-0.91946638E-06 0.82609363E-08 0.36187008E-09 -0.50834173E-08 0.29101141E09
0.33650697E-10 -0.52335129E-07 0.24399612E-10
technical efficiency estimates :
firm
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
eff.-est.
0.17655656E+00
0.99976365E+00
0.59315706E+00
0.37746358E+00
0.28689928E+00
0.64179517E+00
0.51677124E+00
0.44057477E+00
0.33961580E+00
0.72262972E+00
0.49275544E+00
0.45557126E+00
0.79309745E+00
0.85566742E+00
0.71128743E+00
0.86317682E+00
0.77619034E+00
0.43758173E+00
0.90601042E+00
0.83192607E+00
0.56982972E+00
0.57362692E-01
0.47705771E+00
0.42374829E+00
0.48853593E+00
0.44795986E+00
0.54754593E+00
0.88135466E+00
0.32767648E+00
0.44380964E+00
0.18398063E+00
0.13178763E+00
0.89520246E+00
0.77551802E+00
0.75655523E+00
0.28309939E+00
0.21355246E+00
0.25349668E+00
0.70207257E+00
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40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
0.55207011E+00
0.92011685E+00
0.36804674E+00
0.52139955E+00
0.49467517E+00
0.59921185E+00
0.43363587E+00
0.59767721E+00
0.88201839E+00
0.72583980E+00
0.72583980E+00
0.77591695E+00
0.55074862E+00
0.72662682E+00
0.45583457E+00
0.41523143E+00
0.65705232E+00
0.53385501E+00
0.75338624E+00
0.62782187E+00
0.82036816E+00
0.44417236E+00
0.92061128E+00
0.36159735E+00
0.34187210E+00
0.94150703E+00
mean efficiency = 0.57270416E+00
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Lampiran 16. Efisiensi Biaya
Output from the program FRONTIER (Version 4.1c)
instruction file = terminal
data file =
ce.txt
Error Components Frontier (see B&C 1992)
The model is a cost function
The dependent variable is not logged
the ols estimates are :
coefficient
standard-error
t-ratio
beta 0
0.84982766E+06 0.19760031E+07
beta 1
0.85484028E+02 0.10682378E+03
beta 2
0.15251250E+03 0.52017172E+02
beta 3
0.25140276E+02 0.21350122E+02
beta 4
0.12229446E+04 0.47802387E+03
beta 5
0.54036093E+04 0.29807234E+04
beta 6
0.60297779E+03 0.19797786E+03
sigma-squared 0.52023275E+13
0.43007406E+00
0.80023408E+00
0.29319644E+01
0.11775237E+01
0.25583338E+01
0.18128516E+01
0.30456830E+01
log likelihood function = -0.10401319E+04
the estimates after the grid search were :
beta 0
-0.14600961E+07
beta 1
0.85484028E+02
beta 2
0.15251250E+03
beta 3
0.25140276E+02
beta 4
0.12229446E+04
beta 5
0.54036093E+04
beta 6
0.60297779E+03
sigma-squared 0.99778247E+13
gamma
0.84000000E+00
mu is restricted to be zero
eta is restricted to be zero
iteration = 0 func evals = 20 llf = -0.10367505E+04
-0.14600961E+07
0.85484028E+02
0.15251250E+03
0.25140276E+02
0.12229446E+04
0.54036093E+04 0.60297779E+03 0.99778247E+13 0.84000000E+00
gradient step
iteration = 5 func evals = 120 llf = -0.10366821E+04
-0.14600961E+07
0.97229173E+02
0.15037538E+03
0.24937679E+02
0.11852554E+04
0.53874813E+04 0.63233013E+03 0.99778247E+13 0.84996483E+00
iteration = 10 func evals = 260 llf = -0.10366782E+04
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-0.14600961E+07
0.98073061E+02
0.14816355E+03
0.26200900E+02
0.11790952E+04
0.53848002E+04 0.63617845E+03 0.99778247E+13 0.85011294E+00
iteration = 15 func evals = 395 llf = -0.10366693E+04
-0.14600961E+07
0.90335621E+02
0.14974013E+03
0.26796400E+02
0.11604391E+04
0.53756618E+04 0.63972793E+03 0.99778247E+13 0.85159646E+00
iteration = 20 func evals = 547 llf = -0.10366587E+04
-0.14600961E+07
0.95886635E+02
0.15260680E+03
0.26569881E+02
0.11342212E+04
0.53572320E+04 0.62424997E+03 0.99778247E+13 0.85563719E+00
iteration = 25 func evals = 685 llf = -0.10366528E+04
-0.14600960E+07
0.97224952E+02
0.15281030E+03
0.27520236E+02
0.11187637E+04
0.53479663E+04 0.62248539E+03 0.99778247E+13 0.85035938E+00
iteration = 30 func evals = 836 llf = -0.10363934E+04
-0.14600931E+07
0.91566156E+02
0.16417275E+03
0.29254993E+02
0.11646589E+04
0.35826836E+04 0.59668812E+03 0.99778247E+13 0.86344123E+00
iteration = 35 func evals = 988 llf = -0.10363402E+04
-0.14600924E+07
0.89070688E+02
0.15866126E+03
0.30556098E+02
0.12060069E+04
0.31110263E+04 0.61154477E+03 0.99778247E+13 0.87494649E+00
iteration = 40 func evals = 1151 llf = -0.10363286E+04
-0.14600923E+07
0.84758378E+02
0.15897780E+03
0.30423091E+02
0.12079365E+04
0.30739129E+04 0.61287385E+03 0.99778247E+13 0.87228647E+00
iteration = 45 func evals = 1264 llf = -0.10363277E+04
-0.14600924E+07
0.85822324E+02
0.15879742E+03
0.30478636E+02
0.12047371E+04
0.31292578E+04 0.61267234E+03 0.99778247E+13 0.86981695E+00
the final mle estimates are :
coefficient
standard-error
t-ratio
beta 0
-0.14600924E+07 0.46375390E+01 -0.31484207E+06
beta 1
0.85822324E+02 0.84770777E+02 0.10124046E+01
beta 2
0.15879742E+03 0.33090494E+02 0.47988834E+01
beta 3
0.30478636E+02 0.15777989E+02 0.19317187E+01
beta 4
0.12047371E+04 0.32862507E+03 0.36659927E+01
beta 5
0.31292578E+04 0.27578500E+04 0.11346729E+01
beta 6
0.61267234E+03 0.16316096E+03 0.37550180E+01
sigma-squared 0.99778247E+13 0.10000000E+01 0.99778247E+13
gamma
0.86981695E+00 0.63007507E-01 0.13804973E+02
mu is restricted to be zero
eta is restricted to be zero
log likelihood function = -0.10363277E+04
LR test of the one-sided error = 0.76084208E+01
with number of restrictions = 1
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[note that this statistic has a mixed chi-square distribution]
number of iterations =
45
(maximum number of iterations set at : 100)
number of cross-sections =
number of time periods =
65
1
total number of observations =
thus there are:
65
0 obsns not in the panel
covariance matrix :
0.21506768E+02
-0.44584335E+02
0.33103296E+02
0.18394994E+02
0.37585974E+03
-0.12487494E+05 -0.52212146E+02 -0.60171505E-10 0.10496521E+00
-0.44584335E+02
0.71860846E+04 -0.82987176E+03 -0.20196428E+03 0.10388798E+05
0.25986762E+05 0.16738445E+04 0.83109372E-09 -0.93574651E+00
0.33103296E+02 -0.82987176E+03
0.10949808E+04 -0.11979260E+03 0.10146452E+04
-0.20290069E+05 -0.35936661E+04 -0.22753638E-09 0.46708366E-01
0.18394994E+02 -0.20196428E+03 -0.11979260E+03
0.24894494E+03 0.14463354E+04
-0.11420432E+05 -0.16457771E+03 0.19679609E-09 -0.16383508E-01
0.37585974E+03
-0.10388798E+05
-0.10146452E+04
-0.14463354E+04
0.10799444E+06
-0.21643308E+06 -0.69668557E+04 -0.20247160E-08 0.16072777E+01
-0.12487494E+05
0.25986762E+05 -0.20290069E+05 -0.11420432E+05 0.21643308E+06
0.76057366E+07 0.30592030E+05 0.36172787E-07 -0.63983041E+02
-0.52212146E+02
0.16738445E+04 -0.35936661E+04 -0.16457771E+03 0.69668557E+04
0.30592030E+05 0.26621499E+05 -0.96898697E-09 0.11160811E+01
-0.60171505E-10 0.83109372E-09 -0.22753638E-09 0.19679609E-09 -0.20247160E08
0.36172787E-07 -0.96898697E-09 0.10000000E+01 0.12629558E-11
0.10496521E+00
-0.93574651E+00
0.46708366E-01
-0.16383508E-01
0.16072777E+01
-0.63983041E+02 0.11160811E+01 0.12629558E-11 0.39699460E-02
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cost efficiency estimates :
firm
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
eff.-est.
0.16053292E+01
0.11311818E+01
0.15170463E+01
0.17753817E+01
0.12029803E+01
0.11097879E+01
0.12669209E+01
0.12352208E+01
0.13184070E+01
0.11440727E+01
0.12078802E+01
0.12452644E+01
0.11638045E+01
0.11108180E+01
0.10569729E+01
0.10657499E+01
0.11023566E+01
0.11564917E+01
0.10791273E+01
0.11974075E+01
0.12390049E+01
0.16238271E+01
0.12910152E+01
0.12029333E+01
0.12332375E+01
0.11701268E+01
0.12180568E+01
0.10806149E+01
0.16848811E+01
0.11445650E+01
0.12288881E+01
0.12284044E+01
0.10476250E+01
0.11818015E+01
0.11844483E+01
0.13031708E+01
0.12044030E+01
0.12924264E+01
0.11114633E+01
0.11922703E+01
0.10780313E+01
0.12890885E+01
0.12033253E+01
0.11810217E+01
0.10797192E+01
0.14564265E+01
0.15266230E+01
0.11278178E+01
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49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
0.12182411E+01
0.12166252E+01
0.14514238E+01
0.11181698E+01
0.11082843E+01
0.10469225E+01
0.10432699E+01
0.11439679E+01
0.11569011E+01
0.12480089E+01
0.12995103E+01
0.10558574E+01
0.12971666E+01
0.10456274E+01
0.12002279E+01
0.13006443E+01
0.12021132E+01
mean efficiency = 0.12253905E+01
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Lampiran 17. Hasil Regresi Faktor-Faktor yang Mempengaruhi Efisiensi
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT te
/METHOD=ENTER z1 z2 z3 z4 z5
/SCATTERPLOT=(*ZPRED ,*SRESID )
/RESIDUALS DURBIN HIST(ZRESID) NORM(ZRESID)
/SAVE RESID .
Regression
[DataSet0]
Descriptive Statistics
ef isiensi teknis
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
Mean
,5760
9,6000
17,5077
77,4308
St d. Dev iation
,21859
2,89828
13,66465
38,43662
N
2,1385
1,28546
65
,3846
,49029
65
65
65
65
65
Correlati ons
Pearson Correlation
Sig. (1-tailed)
N
ef isiensi teknis
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
ef isiensi teknis
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
ef isiensi teknis
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
ef isiensi
teknis
1,000
-,288
,061
-,069
tingkat
pendidikan
-,288
1,000
-,029
,277
pengalaman
bertani
,061
-,029
1,000
,190
ketersediaan
modal
-,069
,277
,190
1,000
jumlah lahan
y ang
diusahakan
-,140
-,035
-,180
-,336
serangan
hama
-,681
,297
,099
,358
-,140
-,035
-,180
-,336
1,000
-,160
-,681
.
,010
,316
,291
,297
,010
.
,409
,013
,099
,316
,409
.
,064
,358
,291
,013
,064
.
-,160
,133
,390
,075
,003
1,000
,000
,008
,217
,002
,133
,390
,075
,003
.
,101
,000
65
65
65
65
,008
65
65
65
65
,217
65
65
65
65
,002
65
65
65
65
,101
65
65
65
65
.
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
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Variabl es Entered/Removedb
Model
1
Variables
Entered
serangan
hama,
pengalam
an bertani,
jumlah
lahan y ang
diusahaka
n, tingkat
pendidika
n,
ketersediaa
an modal
Variables
Remov ed
Method
.
Enter
a. All requested v ariables entered.
b. Dependent Variable: ef isiensi teknis
Model Summaryb
Model
1
R
,747a
R Square
,557
Adjusted
R Square
,520
St d. Error of
the Estimate
,15146
DurbinWat son
2,068
a. Predictors: (Constant), serangan hama, pengalaman bertani, jumlah
lahan y ang diusahakan, tingkat pendidikan, ketersediaan modal
b. Dependent Variable: ef isiensi teknis
ANOVAb
Model
1
Regression
Residual
Total
Sum of
Squares
1,704
1,354
3,058
df
5
59
64
Mean Square
,341
,023
F
14,859
Sig.
,000a
a. Predictors: (Const ant), serangan hama, pengalaman bertani, jumlah lahan y ang
diusahakan, tingkat pendidikan, ketersediaan modal
b. Dependent Variable: ef isiensi teknis
Coeffi ci entsa
Model
1
(Constant)
tingkat pendidikan
pengalaman bertani
ketersediaan modal
jumlah lahan y ang
diusahakan
serangan hama
Unstandardized
Coef f icients
B
St d. Error
,776
,087
-,009
,007
,001
,001
,001
,001
St andardized
Coef f icients
Beta
Correlations
Part ial
Part
Collinearity Statistics
Tolerance
VI F
Sig.
,000
,221
,463
,151
Zero-order
-,115
,066
,146
t
8,954
-1,237
,739
1,455
-,288
,061
-,069
-,159
,096
,186
-,107
,064
,126
,866
,940
,744
1,154
1,063
1,344
-,034
,016
-,201
-2,161
,035
-,140
-,271
-,187
,868
1,153
-,329
,043
-,738
-7,735
,000
-,681
-,710
-,670
,825
1,212
a. Dependent Variable: ef isiensi teknis
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a
Colli neari ty Diagnostics
Variance Proportions
Model
1
Dimension
1
2
3
4
5
6
Eigenv alue
4,710
,578
,379
,216
,084
,033
Condit ion
Index
1,000
2,855
3,525
4,674
7,497
11,934
tingkat
pendidikan
,00
,00
,00
,02
,36
,61
(Constant)
,00
,00
,00
,00
,04
,95
pengalaman
bertani
,01
,00
,61
,30
,00
,08
ketersediaan
modal
,01
,00
,01
,27
,66
,06
jumlah lahan
y ang
diusahakan
,01
,09
,15
,24
,34
,18
serangan
hama
,01
,63
,09
,23
,00
,03
a. Dependent Variable: ef isiensi teknis
Residual s Stati sticsa
Minimum
,2471
-2,015
Predicted Value
St d. Predicted Value
St andard Error of
Predicted Value
Adjusted Predict ed Value
Residual
St d. Residual
St ud. Residual
Delet ed Residual
St ud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Lev erage Value
Maximum
,7944
1,338
Mean
,5760
,000
St d. Dev iation
,16319
1,000
N
65
65
,033
,064
,045
,008
65
,2049
-,35220
-2,325
-2,382
-,37412
-2,485
2,038
,000
,032
,8141
,36748
2,426
2,576
,41965
2,711
10,496
,174
,164
,5759
,00000
,000
,000
,00014
,001
4,923
,017
,077
,16306
,14543
,960
1,007
,16004
1,027
2,086
,027
,033
65
65
65
65
65
65
65
65
65
a. Dependent Variable: ef isiensi teknis
Charts
Histogram
Dependent Variable: efisiensi teknis
12.5
Frequency
10.0
7.5
5.0
2.5
Mean =1.4
Std. Dev.
N =6
0.0
-3
-2
-1
0
1
2
3
Regression Standardized Residual
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Normal P-P Plot of Regression Standardized Residual
Dependent Variable: efisiensi teknis
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
Scatterplot
Regression Standardized Predicted
Value
Dependent Variable: efisiensi teknis
1
0
-1
-2
-3
-2
-1
0
1
2
Regression Studentized Residual
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