YT = 8.277147 + 0.89070X1 + 0.25941X2 – 0.09346X3 (R2 = 93.91%) YP = -4.71666 + 0.89428X1 + 0.12785X2 – 0.15976X6 (R2 = 92.59%) YG = YT – YP – YC YC = -0.00000236 + 0.85779X6 + 0.32839X7 (R2 = 73.25%) Saran
Peubah kanonik yang dipergunakan dalam analisis data diharapkan hanya menggunakan satu peubah kanonik saja dikarenakan ketidakstabilan peubah kanonik lainnya. Disamping itu, model penduga volume yang dihasilkan diharapkan dapat membantu PT. Sari Bumi Kusuma dan unit perusahaan yang memiliki kondisi area yang relatif sama dalam menduga volume optimal kayu.
DAFTAR PUSTAKA
[ATIBT)] Association Technique Internationale des Bois Tripicaux. Committee V. 1982. The Grading Rules for Tropical Logs and Sawn Timber. Paris (FR): ATIBT. Cahyana I. 2000. Penyusunan model penduga volume optimal batang bebas cabang melalui inventarisasi kualitas pohon dan analisis data peubah ganda. [skripsi]. Bogor (ID): Institut Pertanian Bogor.
Hadikusumo SA, Prawirohatmodjo S. 1986. Hasil Hutan dan Ilmu Kayu. Yogyakarta (ID): Universitas Gadjah Mada.
Husch B. 1971. Forest Mensuration and Statistics. New York: The Ronald Press Company.
Mattjik AA, Sumertajaya IM. 2011. Sidik Peubah Ganda dengan Menggunakan SAS. Bogor (ID): IPB Press.
Priyatno D. 2011. Buku Saku SPSS. Yogyakarta (ID): MediaKom.
Riduwan, Sunarto. 2011. Pengantar Statistika. Bandung (ID): Penerbit Alfabeta. Supranto J. 1989. Statistika Teori dan Aplikasi. Jakarta (ID): Erlangga.
13 Lampiran 1 Spesifikasi Bahan Baku Kayu Bulat menurut Standar ATIBT/1982 a. Mata Kayu Standar
1. Satu mata kayu hidup dengan diameter antara 1 sampai 6 cm untuk setiap 2 m panjang kayu dengan interval mata kayu tidak kurang dari 2 m
2. Satu mata kayu hidup dengan diameter antara 1 sampai dengan 6 cm untuk setiap 1.5 m panjang kayu dengan interval mata kayu tidak kurang dari 2 m 3. Dua mata kayu hidup dengan diameter antara 1 sampai dengan 6 cm untuk
setiap 2 m panjang kayu dengan interval mata kayu tidak kurang dari 1 m 4. Dua mata kayu dengan diameter antara 1 sampai dengan 6 cm untuk setiap
2 m panjang kayu dengan interval mata kayu tidak kurang dari 0.5 m 5. Mata kayu dengan diameter kurang dari 1 cm diabaikan
b. Lubang Gerek Standar
1. Konsentrasi lubang gerek dengan jumlah dari 15 sampai 30 dalam suatu persegi dengan ukuran 125 x 125 mm untuk setiap persegian
2. Untuk setiap penambahan 15 lubang dalam persegi ukuran 125 x 125 mm 3. Lebih dari 3 sampai 10 lubang gerek per 3 m panjang kayu tersebar di
permukaan tetapi tidak sampai pada sapwood
4. Untuk setiap penambahan 10 lubang gerek per 3 m panjang kayu 5. Satu lubang gerek besar 3 m panjang kayu
6. Untuk setiap penambahan 2 lubang per 3 m panjang kayu
7. Lubang gerek kecil tersebar di permukaan tetapi di luar persegian ukuran 125 x 125 mm akan diabaikan
8. Sampai 3 lubang gerek sedang tersebar di permukaan tetapi tidak sampai ke sapwood per 3 m panjang kayu akan diabaikan
c. Melintir Standar
1. Satu atau dua kayu melintir dengan panjang total sampai dengan 10% panjang kayu
2. Satu atau dua kayu melintir dengan panjang total sampai dengan 10% − 20% panjang kayu
3. Dua atau tiga kayu melintir dengan panjang total dari dua kayu melintir terpanjang antara 20% − 40% panjang kayu
4. Tiga atau lebih kayu melintir dengan panjang total dari dua kayu terpanjang lebih dari 40% panjang kayu
d. Bengkok Standar
1. Satu bengkok dengan penyimpanan sebesar 10% dari diameter ujung terpendek
2. Satu bengkok dengan penyimpanan sebesar 10% − 20% dari diameter ujung terpendek
3. Satu bengkok dengan penyimpanan sebesar 30% dari diameter ujung terpendek
4. Satu atau dua bengkok dengan penyimpanan sebesar 40% dari diameter ujung terpendek
14
Lampiran 2 Spesifikasi Bahan Baku Kayu Kupas menurut Standar ATIBT/1982 Faktor
Kualitas
Spesifikasi
Kualitas Prime (SP.1) Kualitas Standard (SP.2)
Panjang 2.5 m atau lebih 2.5 m atau lebih
Diameter (minimum)
50 cm atau lebih 50 cm atau lebih
Bentuk log Fresh cut, silindris, serat lurus Fresh cut, silindris, serat lurus Hati kayu Di tengah, tidak lebih dari ¼
diameter log rata-rata
Di tengah, tidak lebih dari 1/3 diameter log rata-rata.
Diperkenankan lubang kecil di tengah, retak kecil dengan diameter tidak lebih dari 15 cm
Mata kayu Diperbolehkan mengandung 1
unit mata kayu standar
Diperbolehkan mengandung 2 unit mata kayu standar
Lubang gerek Diperbolehkan mengandung
salah satu unit lubang gerek standar
Diperbolehkan mengandung 2 unit lubang gerek standar
Melintir Diperbolehkan mengandung
salah satu unit melintir standar
Diperbolehkan mengandung 2 unit melintir standar
Bengkok Diperbolehkan mengandung
salah satu bengkok standar
Diperbolehkan mengandung 2 bengkok standar
Warna Diperbolehkan, sapwood bebas
cacat
Diperbolehkan, sapwood bebas cacat
Pecah permukaan
Tidak diizinkan Sedikit pecah permukaan
diijinkan, kurang dari ¼ permukaan lateral, kedalaman kurang dari 5 cm
15 Lampiran 3 Spesifikasi Bahan Baku Kayu untuk Kayu Gergajian dan Kayu
Serpih menurut Standar ATIBT/1982 Faktor
Kualitas
Spesifikasi
Kayu Gergajian Kayu Serpih
SS.1 SS.2 SS.3 SS.4 SS.5
Panjang ≥2.5 m ≥2 m ≥2 m ≥2 m 1.20 − 2 m tak bersyarat
Diameter ≥45 cm ≥40 cm ≥30 cm - - tak bersyarat
Bentuk log fresh cut, silindris, serat lurus - - - - tak bersyarat
Hati kayu Ditengah - - - - tak bersyarat
Mata kayu 3 unit mata kayu standar - - - - tak bersyarat Lubang gerek 3 unit lubang gerek standar - - - - tak bersyarat
Melintir 3 unit melintir standar
- - - - tak bersyarat
Bengkok 3 unit bengkok
standar - - - - tak bersyarat Warna diperbolehkan, sapwood bebas cacat - - - - tak bersyarat Lain-lain 75% bebas cacat 60% bebas cacat 50% bebas cacat 40% bebas cacat masih dapat digergaji tak bersyarat
16
Lampiran 4 Rekapitulasi data peubah bebas X dan peubah tak bebas Y Jenis Peubah Nilai Terkecil Nilai Terbesar Rata-rata Simpangan Baku Peubah Bebas X1 43 86 58.98 11.26 X2 15 25 20.43 3.13 X3 0 8 3 2.06 X4 0 1.81 0.22 0.47 X5 47.75 89 69.84 9.04 X6 0 12 2 2.63 X7 0 3 0 0.7 X8 0 28 9.1 9.45 X9 0 30 4.1 9.23
Peubah Tak Bebas
Y1 1.74 9.66 4.21 2.08
Y2 0.31 9.51 3.37 2.6
Y3 0 2.17 0.62 0.65
Y4 0 1.49 0.22 0.32
Keterangan :
X1 = Diameter setinggi dada (cm) X2 = Tinggi bebas cabang (m) X3 = Jumlah banir
X4 = Kelengkungan (cm/m) X5 = Keselindrisan (%) X6 = Jumlah mata kayu sehat X7 = Jumlah mata kayu mati
X8 = Diameter rata-rata mata kayu sehat (cm) X9 = Diameter rata-rata mata kayu mati (cm) Y1 = Volume pohon total (m3)
Y2 = Volume pohon untuk bahan baku plywood (m3) Y3 = Volume pohon untuk bahan baku kayu gergajian (m3) Y4 = Volume pohon untuk bahan baku chipwood (m3)
17 Lampiran 5 Hasil Analisis Kanonik
Canonical Correlation Analysis
Adjusted Approximate Squared Canonical Canonical Standard Canonical Correlation Correlation Error Correlation 1 0.979278 0.972415 0.007616 0.958985 2 0.891286 0.862290 0.038181 0.794392 3 0.654310 0.578586 0.106195 0.428121 Eigenvalues of Inv(E)*H
= CanRsq/(1-CanRsq)
Eigenvalue Difference Proportion Cumulative 1 23.3812 19.5176 0.8352 0.8352 2 3.8636 3.1150 0.1380 0.9733 3 0.7486 0.0267 1.0000 Test of H0: The canonical correlations in the
current row and all that follow are zero Likelihood Approximate
Ratio F Value Num DF Den DF Pr > F 1 0.00482269 10.27 27 53.212 <.0001 2 0.11758308 4.55 16 38 <.0001 3 0.57187863 2.14 7 20 0.0863
18
Canonical Correlation Analysis
Raw Canonical Coefficients for the VAR Variables V1 V2 V3 Y2 -0.380498333 6.9859956993 -0.642329543 Y3 1.6639481036 -8.091010153 -0.208419411 Y4 0.4126524013 -1.736777326 -1.362353875 Raw Canonical Coefficients for the WITH Variables W1 W2 W3 X1 0.862549109 0.1933577407 0.4658349537 X2 0.2168494932 0.204941638 -0.738663813 X3 -0.086438035 0.0305735055 0.3482520974 X4 -0.020401683 -0.098249359 -0.013898461 X5 0.0300068872 0.0724473575 0.2073315283 X6 -0.225022923 0.9284052253 0.0466223454 X7 -0.161538768 0.272737009 0.2091613546 X8 -0.002357156 -0.257776898 -0.174596972 X9 0.1314913045 0.1172350205 -0.139508913
Standardized Canonical Coefficients for the VAR Variables V1 V2 V3
Y2 -0.3805 6.9860 -0.6423 Y3 1.6639 -8.0910 -0.2084 Y4 0.4127 -1.7368 -1.3624
Standardized Canonical Coefficients for the WITH Variables W1 W2 W3 X1 0.8625 0.1934 0.4658 X2 0.2168 0.2049 -0.7387 X3 -0.0864 0.0306 0.3483 X4 -0.0204 -0.0982 -0.0139 X5 0.0300 0.0724 0.2073 X6 -0.2250 0.9284 0.0466 X7 -0.1615 0.2727 0.2092 X8 -0.0024 -0.2578 -0.1746 X9 0.1315 0.1172 -0.1395
19
Canonical Structure
Correlations Between the VAR Variables and Their Canonical Variables
V1 V2 V3 Y2 0.9789 0.2002 0.0412 Y3 0.9763 0.0719 0.2040 Y4 -0.6109 -0.1054 -0.7847 Correlations Between the WITH Variables and Their Canonical Variables
W1 W2 W3 X1 0.9316 0.1916 0.2742 X2 0.4589 0.2210 -0.7013 X3 0.1060 0.1402 0.3899 X4 -0.0897 -0.2774 0.1306 X5 -0.2631 -0.0687 0.6139 X6 -0.3179 0.8345 0.0105 X7 0.0595 0.4474 0.1639 X8 0.1293 0.2155 -0.1028 X9 0.3846 0.4250 -0.2885 Correlations Between the VAR Variables and the Canonical Variables of the WITH Variables W1 W2 W3 Y2 0.9586 0.1785 0.0270 Y3 0.9561 0.0641 0.1335 Y4 -0.5982 -0.0940 -0.5134
Correlations Between the WITH Variables and the Canonical Variables of the VAR Variables V1 V2 V3 X1 0.9123 0.1708 0.1794 X2 0.4494 0.1970 -0.4589 X3 0.1038 0.1250 0.2551 X4 -0.0878 -0.2473 0.0854 X5 -0.2576 -0.0613 0.4017 X6 -0.3113 0.7438 0.0069 X7 0.0583 0.3988 0.1072 X8 0.1266 0.1921 -0.0672 X9 0.3767 0.3788 -0.1888
20
Canonical Redundancy Analysis
Raw Variance of the VAR Variables Explained by Their Own
Canonical Variables Canonical
Variable Cumulative Canonical Number Proportion Proportion R-Square 1 0.7615 0.7615 0.9590 2 0.0188 0.7803 0.7944 3 0.2197 1.0000 0.4281 Raw Variance of the VAR
Variables Explained by The Opposite Canonical Variables Canonical
Variable Cumulative Number Proportion Proportion 1 0.7303 0.7303 2 0.0149 0.7452 3 0.0940 0.8393
Raw Variance of the WITH Variables Explained by Their Own
Canonical Variables Canonical
Variable Cumulative Canonical Number Proportion Proportion R-Square 1 0.1596 0.1596 0.9590 2 0.1456 0.3052 0.7944 3 0.1371 0.4423 0.4281
Raw Variance of the WITH Variables Explained by The Opposite Canonical Variables Canonical
Variable Cumulative Number Proportion Proportion 1 0.1530 0.1530 2 0.1157 0.2687 3 0.0587 0.3274
21
Canonical Redundancy Analysis Standardized Variance of the VAR Variables Explained by Their Own
Canonical Variables Canonical
Variable Cumulative Canonical Number Proportion Proportion R-Square 1 0.7615 0.7615 0.9590 2 0.0188 0.7803 0.7944 3 0.2197 1.0000 0.4281 Standardized Variance of the
VAR Variables Explained by The Opposite Canonical Variables Canonical
Variable Cumulative Number Proportion Proportion 1 0.7303 0.7303 2 0.0149 0.7452 3 0.0940 0.8393
Standardized Variance of the WITH Variables Explained by Their Own
Canonical Variables Canonical
Variable Cumulative Canonical Number Proportion Proportion R-Square 1 0.1596 0.1596 0.9590 2 0.1456 0.3052 0.7944 3 0.1371 0.4423 0.4281
Standardized Variance of the WITH Variables Explained by The Opposite Canonical Variables Canonical
Variable Cumulative Number Proportion Proportion 1 0.1530 0.1530 2 0.1157 0.2687 3 0.0587 0.3274
22
Canonical Redundancy Analysis
Squared Multiple Correlations Between the VAR Variables and the First M Canonical Variables of the WITH Variables M 1 2 3 Y2 0.9189 0.9508 0.9515 Y3 0.9141 0.9182 0.9360 Y4 0.3579 0.3667 0.6303 Squared Multiple Correlations Between the WITH Variables and the First M Canonical Variables of the VAR Variables M 1 2 3 X1 0.8322 0.8614 0.8936 X2 0.2019 0.2408 0.4513 X3 0.0108 0.0264 0.0915 X4 0.0077 0.0688 0.0761 X5 0.0664 0.0701 0.2315 X6 0.0969 0.6502 0.6502 X7 0.0034 0.1624 0.1739 X8 0.0160 0.0529 0.0575 X9 0.1419 0.2854 0.3210
23 Lampiran 6 Hasil Analisis Regresi Berganda
The REG Procedure Model: MODEL1 Dependent Variable: Y1 Number of Observations Read 30 Number of Observations Used 30
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 9 27.59303 3.06589 43.58 Error 20 1.40699 0.07035 Corrected Total 29 29.00002 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total
Root MSE 0.26524 R-Square 0.9515 Dependent Mean 1E-6 Adj R-Sq 0.9297 Coeff Var 26523527
Parameter Estimates Parameter Standard
Variable DF Estimate Error t Value Pr > |t| Intercept 1 0.00000141 0.04843 0.00 1.0000 X1 1 0.87391 0.05704 15.32 <.0001 X2 1 0.22453 0.06707 3.35 0.0032 X3 1 -0.06801 0.05864 -1.16 0.2598 X4 1 -0.03747 0.05648 -0.66 0.5147 X5 1 0.04729 0.06428 0.74 0.4705 X6 1 -0.04875 0.05614 -0.87 0.3956 X7 1 -0.10053 0.06748 -1.49 0.1519 X8 1 -0.05298 0.05895 -0.90 0.3795 X9 1 0.14321 0.07935 1.80 0.0862
24
The REG Procedure Model: MODEL1 Dependent Variable: Y2 Number of Observations Read 30 Number of Observations Used 30
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 9 27.14505 3.01612 32.52 Error 20 1.85494 0.09275 Corrected Total 29 28.99999 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total
Root MSE 0.30454 R-Square 0.9360 Dependent Mean -3.33333E-7 Adj R-Sq 0.9073 Coeff Var -91363154
Parameter Estimates Parameter Standard
Variable DF Estimate Error t Value Pr > |t| Intercept 1 4.039428E-8 0.05560 0.00 1.0000 X1 1 0.89926 0.06549 13.73 <.0001 X2 1 0.12186 0.07701 1.58 0.1292 X3 1 -0.03419 0.06733 -0.51 0.6171 X4 1 -0.02766 0.06485 -0.43 0.6743 X5 1 0.06101 0.07380 0.83 0.4182 X6 1 -0.14939 0.06447 -2.32 0.0312 X7 1 -0.10904 0.07748 -1.41 0.1747 X8 1 -0.04209 0.06769 -0.62 0.5411 X9 1 0.11461 0.09111 1.26 0.2229
25
The REG Procedure Model: MODEL1 Dependent Variable: Y3 Number of Observations Read 30 Number of Observations Used 30
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 9 18.27878 2.03098 3.79 Error 20 10.72127 0.53606 Corrected Total 29 29.00005 Analysis of Variance Source Pr > F Model 0.0063 Error Corrected Total
Root MSE 0.73216 R-Square 0.6303 Dependent Mean -0.00000133 Adj R-Sq 0.4639 Coeff Var -54912262
Parameter Estimates Parameter Standard
Variable DF Estimate Error t Value Pr > |t| Intercept 1 -7.02107E-7 0.13367 -0.00 1.0000 X1 1 -0.77334 0.15745 -4.91 <.0001 X2 1 0.23026 0.18514 1.24 0.2280 X3 1 -0.12996 0.16187 -0.80 0.4315 X4 1 0.02857 0.15591 0.18 0.8564 X5 1 -0.13121 0.17744 -0.74 0.4682 X6 1 0.02345 0.15498 0.15 0.8813 X7 1 -0.03638 0.18628 -0.20 0.8472 X8 1 0.11527 0.16273 0.71 0.4869 X9 1 -0.01805 0.21903 -0.08 0.9351
26
The REG Procedure Model: MODEL1 Dependent Variable: Y4 Number of Observations Read 30 Number of Observations Used 30
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 9 23.10293 2.56699 8.71 Error 20 5.89708 0.29485 Corrected Total 29 29.00001 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total
Root MSE 0.54300 R-Square 0.7967 Dependent Mean -6.66667E-7 Adj R-Sq 0.7051 Coeff Var -81450712
Parameter Estimates Parameter Standard
Variable DF Estimate Error t Value Pr > |t| Intercept 1 -0.00000230 0.09914 -0.00 1.0000 X1 1 -0.04725 0.11677 -0.40 0.6900 X2 1 0.00718 0.13731 0.05 0.9588 X3 1 0.09694 0.12005 0.81 0.4289 X4 1 -0.07673 0.11563 -0.66 0.5146 X5 1 0.07734 0.13159 0.59 0.5633 X6 1 0.84360 0.11494 7.34 <.0001 X7 1 0.30256 0.13816 2.19 0.0405 X8 1 -0.23496 0.12068 -1.95 0.0657 X9 1 0.03827 0.16244 0.24 0.8161
27 Lampiran 7 Hasil Analisis Seleksi Stepwise
The REG Procedure Model: MODEL1 Dependent Variable: Y1
Number of Observations Read 30 Number of Observations Used 30 Stepwise Selection: Step 1
Variable X1 Entered: R-Square = 0.8735 and C(p) = 26.1668
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 1 25.33010 25.33010 193.26 Error 28 3.66992 0.13107 Corrected Total 29 29.00002 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept 6.884713E-7 0.06610 1.42198E-11 0.00 1.0000 X1 0.93459 0.06723 25.33010 193.26 <.0001 Bounds on condition number: 1, 1
---
Stepwise Selection: Step 2
Variable X2 Entered: R-Square = 0.9308 and C(p) = 4.5078
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 2 26.99451 13.49725 181.71 Error 27 2.00552 0.07428
28 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept 7.085701E-7 0.04976 1.50621E-11 0.00 1.0000 X1 0.87429 0.05219 20.84655 280.65 <.0001 X2 0.24704 0.05219 1.66440 22.41 <.0001 Bounds on condition number: 1.0633, 4.2534
---
Stepwise Selection: Step 3
Variable X3 Entered: R-Square = 0.9391 and C(p) = 3.1220
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 3 27.23270 9.07757 133.54 Error 26 1.76732 0.06797 Corrected Total 29 29.00002 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept 8.277147E-7 0.04760 2.05533E-11 0.00 1.0000 X1 0.89070 0.05069 20.98926 308.78 <.0001 X2 0.25941 0.05036 1.80366 26.53 <.0001 X3 -0.09346 0.04993 0.23819 3.50 0.0725 Bounds on condition number: 1.0961, 9.7248
---
29
level.
No other variable met the 0.1500 significance level for entry into the model.
Summary of Stepwise Selection Variable Variable Number Partial Model
Step Entered Removed Vars In R-Square R-Square C(p) F Value 1 X1 1 0.8735 0.8735 26.1668 193.26 2 X2 2 0.0574 0.9308 4.5078 22.41 3 X3 3 0.0082 0.9391 3.1220 3.50 Summary of Stepwise Selection
Step Pr > F 1 <.0001 2 <.0001 3 0.0725
30
The REG Procedure Model: MODEL1 Dependent Variable: Y2
Number of Observations Read 30 Number of Observations Used 30 Stepwise Selection: Step 1
Variable X1 Entered: R-Square = 0.8828 and C(p) = 10.6587
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 1 25.60000 25.60000 210.82 Error 28 3.39998 0.12143 Corrected Total 29 28.99999 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept -6.46517E-7 0.06362 1.25395E-11 0.00 1.0000 X1 0.93955 0.06471 25.60000 210.82 <.0001 Bounds on condition number: 1, 1
---
Stepwise Selection: Step 2
Variable X6 Entered: R-Square = 0.9106 and C(p) = 3.9420
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 2 26.40845 13.20423 137.57 Error 27 2.59154 0.09598
31 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept -4.7398E-7 0.05656 6.73971E-12 0.00 1.0000 X1 0.92479 0.05775 24.60961 256.40 <.0001 X6 -0.16762 0.05775 0.80845 8.42 0.0073 Bounds on condition number: 1.0078, 4.0313
---
Stepwise Selection: Step 3
Variable X2 Entered: R-Square = 0.9259 and C(p) = 1.1549
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 3 26.85244 8.95081 108.37 Error 26 2.14755 0.08260 Corrected Total 29 28.99999 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept -4.71666E-7 0.05247 6.67408E-12 0.00 1.0000 X1 0.89428 0.05517 21.70294 262.75 <.0001 X2 0.12785 0.05514 0.44399 5.38 0.0286 X6 -0.15976 0.05368 0.73151 8.86 0.0062 Bounds on condition number: 1.0686, 9.4442
32
All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection Variable Variable Number Partial Model
Step Entered Removed Vars In R-Square R-Square C(p) F Value 1 X1 1 0.8828 0.8828 10.6587 210.82 2 X6 2 0.0279 0.9106 3.9420 8.42 3 X2 3 0.0153 0.9259 1.1549 5.38 Summary of Stepwise Selection
Step Pr > F 1 <.0001 2 0.0073 3 0.0286
33
The REG Procedure Model: MODEL1 Dependent Variable: Y3
Number of Observations Read 30 Number of Observations Used 30 Stepwise Selection: Step 1
Variable X1 Entered: R-Square = 0.5128 and C(p) = 0.3569
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 1 14.87105 14.87105 29.47 Error 28 14.12899 0.50461 Corrected Total 29 29.00005 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept -0.00000109 0.12969 3.59467E-11 0.00 1.0000 X1 -0.71610 0.13191 14.87105 29.47 <.0001 Bounds on condition number: 1, 1
---
Stepwise Selection: Step 2
Variable X5 Entered: R-Square = 0.5802 and C(p) = -1.2919
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 2 16.82708 8.41354 18.66 Error 27 12.17297 0.45085
34 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept -0.00000108 0.12259 3.51047E-11 0.00 1.0000 X1 -0.75478 0.12606 16.16265 35.85 <.0001 X5 -0.26258 0.12606 1.95603 4.34 0.0469 Bounds on condition number: 1.0222, 4.0888
---
All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection Variable Variable Number Partial Model
Step Entered Removed Vars In R-Square R-Square C(p) F Value 1 X1 1 0.5128 0.5128 0.3569 29.47 2 X5 2 0.0674 0.5802 -1.2919 4.34 Summary of Stepwise Selection
Step Pr > F 1 <.0001 2 0.0469
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The REG Procedure Model: MODEL1 Dependent Variable: Y4
Number of Observations Read 30 Number of Observations Used 30 Stepwise Selection: Step 1
Variable X6 Entered: R-Square = 0.6242 and C(p) = 10.9577
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 1 18.10288 18.10288 46.52 Error 28 10.89714 0.38918 Corrected Total 29 29.00001 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept -0.00000146 0.11390 6.3664E-11 0.00 1.0000 X6 0.79009 0.11585 18.10288 46.52 <.0001 Bounds on condition number: 1, 1
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Stepwise Selection: Step 2
Variable X7 Entered: R-Square = 0.7325 and C(p) = 2.3108
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Model 2 21.24215 10.62108 36.96 Error 27 7.75786 0.28733
36 Analysis of Variance Source Pr > F Model <.0001 Error Corrected Total Parameter Standard
Variable Estimate Error Type II SS F Value Pr > F Intercept -0.00000177 0.09787 9.37567E-11 0.00 1.0000 X6 0.77163 0.09969 17.21289 59.91 <.0001 X7 0.32953 0.09969 3.13927 10.93 0.0027 Bounds on condition number: 1.0031, 4.0126
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All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection Variable Variable Number Partial Model
Step Entered Removed Vars In R-Square R-Square C(p) F Value 1 X6 1 0.6242 0.6242 10.9577 46.52 2 X7 2 0.1083 0.7325 2.3108 10.93 Summary of Stepwise Selection
Step Pr > F 1 <.0001 2 0.0027
37 Lampiran 8 Uji Hipotesis Data Peubah Ganda (Multivariate Test)
The REG Procedure Model: MODEL1 Multivariate Test 1
Multivariate Statistics and F Approximations S=3 M=2.5 N=8
Statistic Value F Value Num DF Den DF Wilks' Lambda 0.00482269 10.27 27 53.212 Pillai's Trace 2.18149776 5.92 27 60 Hotelling-Lawley Trace 27.99346218 17.65 27 33.122 Roy's Greatest Root 23.38122600 51.96 9 20 NOTE: F Statistic for Roy's Greatest Root is an upper bound. Multivariate Statistics and F Approximations
S=3 M=2.5 N=8 Statistic Pr > F Wilks' Lambda <.0001 Pillai's Trace <.0001 Hotelling-Lawley Trace <.0001 Roy's Greatest Root <.0001 NOTE: F Statistic for Roy's Greatest Root is an upper bound
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