Regression Analysis: Cstandkop versus k, a
The regression equation is Cstandkop = 82.7 + 0.00446 k + 350 a
Predictor Coef SE Coef T P VIF Constant 82.685 5.628 14.69 0.000
k 0.0044617 0.0009437 4.73 0.000 2.9 a 350.00 44.05 7.94 0.000 2.9
S = 8.529 R-Sq = 79.9 R-Sqadj = 77.7 PRESS = 1790.52 R-Sqpred = 72.51
Analysis of Variance Source DF SS MS F P
Regression 2 5205.2 2602.6 35.78 0.000 Residual Error 18 1309.3 72.7
Total 20 6514.5 Source DF Seq SS
k 1 613.9 a 1 4591.3
Unusual Observations Obs k Cstandko Fit SE Fit Residual St Resid
3 13170 42.87 47.65 6.51 -4.78 -0.87 X 11 558 27.76 47.02 2.54 -19.26 -2.37R
16 368 81.15 62.98 3.59 18.17 2.35R R denotes an observation with a large standardized residual
X denotes an observation whose X value gives it large influence.
Regression Analysis: Cstand versus k, a
The regression equation is Cstand = 71.4 + 0.00411 k + 315 a
Predictor Coef SE Coef T P VIF Constant 71.376 5.124 13.93 0.000
k 0.0041147 0.0008592 4.79 0.000 2.9 a 315.18 40.11 7.86 0.000 2.9
S = 7.766 R-Sq = 79.3 R-Sqadj = 77.0 PRESS = 1602.93 R-Sqpred = 69.42
Analysis of Variance Source DF SS MS F P
Regression 2 4156.9 2078.5 34.47 0.000 Residual Error 18 1085.5 60.3
Total 20 5242.4 Source DF Seq SS
k 1 433.6 a 1 3723.4
Unusual Observations Obs k Cstand Fit SE Fit Residual St Resid
3 13170 34.40 41.10 5.92 -6.70 -1.33 X 11 558 22.05 39.32 2.31 -17.27 -2.33R
16 368 71.00 53.66 3.27 17.34 2.46R R denotes an observation with a large standardized residual
X denotes an observation whose X value gives it large influence.
Regression Analysis: Chidup versus k, a
The regression equation is Chidup = 82.9 + 0.00451 k + 350 a
Predictor Coef SE Coef T P VIF Constant 82.910 5.553 14.93 0.000
k 0.0045116 0.0009311 4.85 0.000 2.9 a 349.98 43.47 8.05 0.000 2.9
S = 8.415 R-Sq = 80.2 R-Sqadj = 78.0 PRESS = 1750.08 R-Sqpred = 72.83
Analysis of Variance Source DF SS MS F P
Regression 2 5166.9 2583.5 36.48 0.000 Residual Error 18 1274.6 70.8
Total 20 6441.5 Source DF Seq SS
k 1 576.2 a 1 4590.8
Unusual Observations Obs k Chidup Fit SE Fit Residual St Resid
3 13170 43.59 48.53 6.42 -4.94 -0.91 X 11 558 27.85 47.28 2.51 -19.43 -2.42R
16 368 81.15 63.22 3.55 17.93 2.35R R denotes an observation with a large standardized residual
X denotes an observation whose X value gives it large influence. 2. AGROFORESTRI DS KERTAYASA
Descriptive Statistics: k, a, CtotAG, Cstandkopi, Cstand, Clive
Variable N Mean Median TrMean StDev SE Mean k 20 5522 4795 5347 3317 742
a 20 -0.2061 -0.2230 -0.2087 0.0486 0.0109 CtotAG 20 40.74 34.11 39.93 17.39 3.89
Cstandk 20 38.16 31.88 37.28 16.84 3.76 Cstand 20 34.48 29.84 33.91 14.84 3.32
Clive 20 38.51 32.27 37.67 16.81 3.76 Variable Minimum Maximum Q1 Q3
k 888 13317 2579 8244 a -0.2590 -0.1070 -0.2453 -0.1813
CtotAG 9.10 86.95 30.76 54.75 Cstandk 8.49 83.64 28.16 50.57
Cstand 8.49 70.79 25.67 47.97 Clive 8.53 83.67 28.45 50.94
Correlations: k, a, CtotAG, Cstandkopi, Cstand, Clive
k a CtotAG Cstandk Cstand a -0.680
0.001 CtotAG -0.162 0.666
0.496 0.001 Cstandk -0.182 0.680 0.998
0.443 0.001 0.000 Cstand -0.068 0.590 0.985 0.982
0.774 0.006 0.000 0.000 Clive -0.175 0.675 0.999 1.000 0.983
0.460 0.001 0.000 0.000 0.000 Cell Contents: Pearson correlation
P-Value
Regression Analysis: CtotAG versus k, a
The regression equation is CtotAG = 101 + 0.00284 k + 370 a
Predictor Coef SE Coef T P Constant 101.36 12.39 8.18 0.000
k 0.002841 0.001094 2.60 0.019 a 370.24 74.63 4.96 0.000
S = 11.60 R-Sq = 60.2 R-Sqadj = 55.5 Analysis of Variance
Source DF SS MS F P Regression 2 3461.5 1730.8 12.86 0.000
Residual Error 17 2287.4 134.6 Total 19 5748.9
Source DF Seq SS k 1 150.0
a 1 3311.6 Unusual Observations
Obs k CtotAG Fit SE Fit Residual St Resid 14 888 42.08 64.27 6.01 -22.19 -2.24R
17 1341 86.95 64.08 5.82 22.87 2.28R R denotes an observation with a large standardized residual
Regression Analysis: Cstandkopi versus k, a
The regression equation is Cstandkopi = 97.3 + 0.00264 k + 358 a
Predictor Coef SE Coef T P Constant 97.33 11.91 8.17 0.000
k 0.002641 0.001051 2.51 0.022 a 357.87 71.75 4.99 0.000
S = 11.15 R-Sq = 60.7 R-Sqadj = 56.1 Analysis of Variance
Source DF SS MS F P Regression 2 3272.2 1636.1 13.16 0.000
Residual Error 17 2114.2 124.4 Total 19 5386.5
Source DF Seq SS k 1 178.2
a 1 3094.0 Unusual Observations
Obs k Cstandk Fit SE Fit Residual St Resid 14 888 40.01 61.38 5.78 -21.37 -2.24R
17 1341 83.64 61.15 5.60 22.49 2.33R R denotes an observation with a large standardized residual
Regression Analysis: Cstand versus k, a
The regression equation is Cstand = 82.8 + 0.00277 k + 308 a
Predictor Coef SE Coef T P Constant 82.77 11.20 7.39 0.000
k 0.0027653 0.0009884 2.80 0.012 a 308.38 67.45 4.57 0.000
S = 10.48 R-Sq = 55.4 R-Sqadj = 50.1 PRESS = 2827.04 R-Sqpred = 32.45
Analysis of Variance Source DF SS MS F P
Regression 2 2317.0 1158.5 10.54 0.001 Residual Error 17 1868.3 109.9
Total 19 4185.3 Source DF Seq SS
k 1 19.6 a 1 2297.4
Unusual Observations Obs k Cstand Fit SE Fit Residual St Resid
14 888 31.32 52.23 5.43 -20.91 -2.33R 17 1341 70.79 52.25 5.26 18.54 2.05R
R denotes an observation with a large standardized residual
Regression Analysis: Cstand versus k, a
The regression equation is Cstand = 82.8 + 0.00277 k + 308 a
Predictor Coef SE Coef T P Constant 82.77 11.20 7.39 0.000
k 0.0027653 0.0009884 2.80 0.012 a 308.38 67.45 4.57 0.000
S = 10.48 R-Sq = 55.4 R-Sqadj = 50.1 PRESS = 2827.04 R-Sqpred = 32.45
Analysis of Variance Source DF SS MS F P
Regression 2 2317.0 1158.5 10.54 0.001 Residual Error 17 1868.3 109.9
Total 19 4185.3 Source DF Seq SS
k 1 19.6 a 1 2297.4
Unusual Observations Obs k Cstand Fit SE Fit Residual St Resid
14 888 31.32 52.23 5.43 -20.91 -2.33R 17 1341 70.79 52.25 5.26 18.54 2.05R
R denotes an observation with a large standardized residual
Regression Analysis: Clive versus k, a
The regression equation is Clive = 97.4 + 0.00267 k + 357 a
Predictor Coef SE Coef T P Constant 97.38 11.93 8.16 0.000
k 0.002669 0.001053 2.54 0.021 a 357.15 71.85 4.97 0.000
S = 11.17 R-Sq = 60.5 R-Sqadj = 55.8 PRESS = 3265.06 R-Sqpred = 39.16
Analysis of Variance Source DF SS MS F P
Regression 2 3246.5 1623.3 13.02 0.000 Residual Error 17 2120.0 124.7
Total 19 5366.6 Source DF Seq SS
k 1 165.0 a 1 3081.6
Unusual Observations Obs k Clive Fit SE Fit Residual St Resid
14 888 40.19 61.54 5.79 -21.35 -2.24R 17 1341 83.67 61.32 5.60 22.35 2.31R
R denotes an observation with a large standardized residual
Lampiran 7 Plot peluang normal sisaan dari persamaan matematik pendugaan potensi persediaan karbon dengan peubah struktur tegakan, a Tegakan murni,
b Tegakan campuran
a
a b
b
Lampiran 8 Plot tebaran nilai sisaan baku dari persamaan matematik pendugaan
potensi persediaan karbon dengan peubah struktur tegakan, a Tegakan murni, b Tegakan campuran
a
b
Lampiran 9. Hasil pengolahan data model hubungan persediaan karbon tegakan dengan dimensi tegakan agroforestri
1. AGROFORESTRI DS PECEKELAN
Descriptive Statistics: CtotAG, Cstandkop, Cstand, Clive, Age, N, D, BA
Variable N Mean Median TrMean StDev SE Mean CtotAG 21 40.58 35.47 39.78 18.74 4.09
Cstandko 21 37.77 32.25 36.99 18.05 3.94 Cstand 21 31.27 26.55 30.62 16.19 3.53
Clive 21 38.17 33.34 37.43 17.95 3.92 Age 21 5.881 5.500 5.816 2.872 0.627
N 21 949.4 925.0 928.9 320.4 69.9 D 21 12.500 12.200 12.447 2.571 0.561
BA 21 15.30 14.87 15.32 5.54 1.21 Variable Minimum Maximum Q1 Q3
CtotAG 11.06 85.37 28.23 48.06 Cstandko 9.26 81.15 26.06 44.56
Cstand 3.87 71.00 21.05 38.60 Clive 9.30 81.15 26.85 45.23
Age 1.500 11.500 3.500 7.500 N 488.0 1800.0 706.0 1187.5
D 8.300 17.700 10.550 13.950 BA 2.86 27.33 11.52 19.46
Correlations: CtotAG, Cstandkop, Cstand, Clive, Age, N, D, BA
CtotAG Cstandko Cstand Clive Age N D Cstandko 0.998
0.000 Cstand 0.987 0.987
0.000 0.000 Clive 0.998 1.000 0.989
0.000 0.000 0.000 Age 0.906 0.906 0.921 0.907
0.000 0.000 0.000 0.000 N -0.076 -0.067 -0.021 -0.058 -0.187
0.743 0.774 0.927 0.804 0.417 D 0.882 0.875 0.867 0.876 0.848 -0.381
0.000 0.000 0.000 0.000 0.000 0.089 BA 0.927 0.930 0.953 0.934 0.826 0.250 0.757
0.000 0.000 0.000 0.000 0.000 0.275 0.000 Cell Contents: Pearson correlation
P-Value
Best Subsets Regression: CtotAG versus Age, N, D, BA
Response is CtotAG A
g B Vars R-Sq R-Sqadj C-p S e N D A
1 86.0 85.2 42.0 7.1993 X 1 82.1 81.2 58.3 8.1346 X
2 96.1 95.6 1.5 3.9092 X X 2 93.6 92.9 12.0 5.0009 X X
3 96.2 95.5 3.2 3.9825 X X X 3 96.1 95.5 3.3 3.9947 X X X
4 96.2 95.2 5.0 4.0849 X X X X
Regression Analysis: CtotAG versus Age, N, D, BA
The regression equation is CtotAG = 11.1 + 0.418 Age - 0.0201 N - 0.50 D + 3.42 BA
Predictor Coef SE Coef T P VIF Constant 11.10 14.22 0.78 0.446
Age 0.4178 0.8234 0.51 0.619 6.7 N -0.020075 0.007840 -2.56 0.021 7.6
D -0.502 1.259 -0.40 0.695 12.6 BA 3.4219 0.7277 4.70 0.000 19.5
S = 4.085 R-Sq = 96.2 R-Sqadj = 95.2 PRESS = 441.908 R-Sqpred = 93.71
Analysis of Variance Source DF SS MS F P
Regression 4 6756.0 1689.0 101.22 0.000 Residual Error 16 267.0 16.7
Total 20 7023.0 No replicates. Cannot do pure error test.
Source DF Seq SS Age 1 5765.7
N 1 63.2 D 1 558.1
BA 1 369.0 Unusual Observations
Obs Age CtotAG Fit SE Fit Residual St Resid 21 1.5 26.780 18.338 1.773 8.442 2.29R
R denotes an observation with a large standardized residual
Regression Analysis: CtotAG versus Age, N, BA
The regression equation is CtotAG = 5.64 + 0.466 Age - 0.0174 N + 3.19 BA
Predictor Coef SE Coef T P VIF Constant 5.641 3.724 1.51 0.148
Age 0.4655 0.7943 0.59 0.565 6.6 N -0.017449 0.004142 -4.21 0.001 2.2
BA 3.1874 0.4175 7.63 0.000 6.8 S = 3.983 R-Sq = 96.2 R-Sqadj = 95.5
PRESS = 408.496 R-Sqpred = 94.18 Analysis of Variance
Source DF SS MS F P Regression 3 6753.3 2251.1 141.93 0.000
Residual Error 17 269.6 15.9 Total 20 7023.0
No replicates. Cannot do pure error test. Source DF Seq SS
Age 1 5765.7 N 1 63.2
BA 1 924.4 Unusual Observations
Obs Age CtotAG Fit SE Fit Residual St Resid 21 1.5 26.780 18.447 1.707 8.333 2.32R
R denotes an observation with a large standardized residual
Regression Analysis: CtotAG versus N, BA
The regression equation is CtotAG = 6.60 - 0.0192 N + 3.41 BA
Predictor Coef SE Coef T P VIF Constant 6.605 3.280 2.01 0.059
N -0.019199 0.002817 -6.81 0.000 1.1 BA 3.4120 0.1629 20.95 0.000 1.1
S = 3.909 R-Sq = 96.1 R-Sqadj = 95.6 PRESS = 374.921 R-Sqpred = 94.66
Analysis of Variance Source DF SS MS F P
Regression 2 6747.9 3373.9 220.78 0.000 Residual Error 18 275.1 15.3
Total 20 7023.0 No replicates. Cannot do pure error test.
Source DF Seq SS N 1 40.7
BA 1 6707.2 Unusual Observations
Obs N CtotAG Fit SE Fit Residual St Resid 21 983 26.780 19.054 1.333 7.726 2.10R
R denotes an observation with a large standardized residual
Regression Analysis: CtotAG versus BA
The regression equation is CtotAG = - 7.38 + 3.13 BA
Predictor Coef SE Coef T P Constant -7.380 4.713 -1.57 0.134
BA 3.1347 0.2904 10.79 0.000 S = 7.199 R-Sq = 86.0 R-Sqadj = 85.2
PRESS = 1293.85 R-Sqpred = 81.58 Analysis of Variance
Source DF SS MS F P Regression 1 6038.2 6038.2 116.50 0.000
Residual Error 19 984.8 51.8 Total 20 7023.0
No replicates. Cannot do pure error test. Unusual Observations
Obs BA CtotAG Fit SE Fit Residual St Resid 1 2.9 11.06 1.59 3.94 9.47 1.57 X
X denotes an observation whose X value gives it large influence.
Regression Analysis: CtotAG versus Age
The regression equation is CtotAG = 5.81 + 5.91 Age
Predictor Coef SE Coef T P Constant 5.810 4.126 1.41 0.175
Age 5.9122 0.6334 9.33 0.000 S = 8.135 R-Sq = 82.1 R-Sqadj = 81.2
PRESS = 1579.31 R-Sqpred = 77.51 Analysis of Variance
Source DF SS MS F P Regression 1 5765.7 5765.7 87.13 0.000
Residual Error 19 1257.2 66.2 Lack of Fit 4 519.6 129.9 2.64 0.075
Pure Error 15 737.7 49.2 Total 20 7023.0
Unusual Observations Obs Age CtotAG Fit SE Fit Residual St Resid
11 7.5 29.20 50.15 2.05 -20.95 -2.66R R denotes an observation with a large standardized residual
Best Subsets Regression: Cstandkop versus Age, N, D, BA
Response is Cstandko A
g B Vars R-Sq R-Sqadj C-p S e N D A
1 86.5 85.8 40.1 6.8108 X 1 82.1 81.1 58.7 7.8442 X
2 96.0 95.6 1.8 3.7996 X X 2 93.4 92.6 13.0 4.9031 X X
3 96.1 95.5 3.3 3.8472 X X X 3 96.1 95.4 3.4 3.8624 X X X
4 96.2 95.3 5.0 3.9304 X X X X
Regression Analysis: Cstandkop versus Age, N, D, BA
The regression equation is Cstandkop = 12.0 + 0.425 Age - 0.0202 N - 0.78 D + 3.41 BA
Predictor Coef SE Coef T P VIF Constant 12.00 13.68 0.88 0.393
Age 0.4245 0.7923 0.54 0.599 6.7 N -0.020181 0.007544 -2.68 0.017 7.6
D -0.782 1.212 -0.65 0.528 12.6 BA 3.4117 0.7002 4.87 0.000 19.5
S = 3.930 R-Sq = 96.2 R-Sqadj = 95.3 PRESS = 419.225 R-Sqpred = 93.56
Analysis of Variance Source DF SS MS F P
Regression 4 6267.3 1566.8 101.42 0.000 Residual Error 16 247.2 15.4
Total 20 6514.5 No replicates. Cannot do pure error test.
Source DF Seq SS Age 1 5345.4
N 1 71.0 D 1 484.2
BA 1 366.8 Unusual Observations
Obs Age Cstandko Fit SE Fit Residual St Resid 21 1.5 23.800 16.228 1.706 7.572 2.14R
R denotes an observation with a large standardized residual
Regression Analysis: Cstandkop versus Age, N, BA
The regression equation is Cstandkop = 3.50 + 0.499 Age - 0.0161 N + 3.05 BA
Predictor Coef SE Coef T P VIF Constant 3.500 3.612 0.97 0.346
Age 0.4989 0.7703 0.65 0.526 6.6 N -0.016091 0.004017 -4.01 0.001 2.2
BA 3.0466 0.4049 7.52 0.000 6.8 S = 3.862 R-Sq = 96.1 R-Sqadj = 95.4
PRESS = 395.651 R-Sqpred = 93.93 Analysis of Variance
Source DF SS MS F P Regression 3 6260.9 2087.0 139.90 0.000
Residual Error 17 253.6 14.9 Total 20 6514.5
No replicates. Cannot do pure error test. Source DF Seq SS
Age 1 5345.4 N 1 71.0
BA 1 844.5 Unusual Observations
Obs Age Cstandko Fit SE Fit Residual St Resid 21 1.5 23.800 16.399 1.656 7.401 2.12R
R denotes an observation with a large standardized residual
Regression Analysis: Cstandkop versus N, BA
The regression equation is Cstandkop = 4.53 - 0.0180 N + 3.29 BA
Predictor Coef SE Coef T P VIF Constant 4.533 3.188 1.42 0.172
N -0.017966 0.002738 -6.56 0.000 1.1 BA 3.2872 0.1583 20.77 0.000 1.1
S = 3.800 R-Sq = 96.0 R-Sqadj = 95.6 PRESS = 370.438 R-Sqpred = 94.31
Analysis of Variance Source DF SS MS F P
Regression 2 6254.6 3127.3 216.62 0.000 Residual Error 18 259.9 14.4
Total 20 6514.5 No replicates. Cannot do pure error test.
Source DF Seq SS N 1 29.0
BA 1 6225.6
Regression Analysis: Cstandkop versus BA
The regression equation is Cstandkop = - 8.55 + 3.03 BA
Predictor Coef SE Coef T P Constant -8.553 4.459 -1.92 0.070
BA 3.0277 0.2747 11.02 0.000 S = 6.811 R-Sq = 86.5 R-Sqadj = 85.8
PRESS = 1168.00 R-Sqpred = 82.07 Analysis of Variance
Source DF SS MS F P Regression 1 5633.1 5633.1 121.44 0.000
Residual Error 19 881.3 46.4 Total 20 6514.5
No replicates. Cannot do pure error test. Unusual Observations
Obs BA Cstandko Fit SE Fit Residual St Resid 1 2.9 9.26 0.11 3.73 9.15 1.61 X
X denotes an observation whose X value gives it large influence.
Regression Analysis: Cstandkop versus Age
The regression equation is Cstandkop = 4.29 + 5.69 Age
Predictor Coef SE Coef T P Constant 4.291 3.979 1.08 0.294
Age 5.6926 0.6108 9.32 0.000 S = 7.844 R-Sq = 82.1 R-Sqadj = 81.1
PRESS = 1474.38 R-Sqpred = 77.37
Analysis of Variance Source DF SS MS F P
Regression 1 5345.4 5345.4 86.87 0.000 Residual Error 19 1169.1 61.5
Lack of Fit 4 462.7 115.7 2.46 0.091 Pure Error 15 706.4 47.1
Total 20 6514.5 Unusual Observations
Obs Age Cstandko Fit SE Fit Residual St Resid 11 7.5 27.76 46.99 1.98 -19.23 -2.53R
R denotes an observation with a large standardized residual
Regression Analysis: Cstandkop versus Age, BA
The regression equation is Cstandkop = - 6.73 + 2.72 Age + 1.86 BA
Predictor Coef SE Coef T P VIF Constant -6.734 3.460 -1.95 0.067
Age 2.7236 0.7233 3.77 0.001 3.2 BA 1.8619 0.3747 4.97 0.000 3.2
S = 5.233 R-Sq = 92.4 R-Sqadj = 91.6 PRESS = 735.970 R-Sqpred = 88.70
Analysis of Variance Source DF SS MS F P
Regression 2 6021.5 3010.8 109.93 0.000 Residual Error 18 493.0 27.4
Total 20 6514.5 No replicates. Cannot do pure error test.
Source DF Seq SS Age 1 5345.4
BA 1 676.1
Regression Analysis: Cstand versus Age, N, D, BA
The regression equation is Cstand = 5.31 + 0.843 Age - 0.0146 N - 0.767 D + 2.90 BA
Predictor Coef SE Coef T P VIF Constant 5.311 7.561 0.70 0.493
Age 0.8433 0.4378 1.93 0.072 6.7 N -0.014557 0.004168 -3.49 0.003 7.6
D -0.7673 0.6695 -1.15 0.269 12.6 BA 2.9026 0.3869 7.50 0.000 19.5
S = 2.172 R-Sq = 98.6 R-Sqadj = 98.2 PRESS = 144.921 R-Sqpred = 97.24
Analysis of Variance Source DF SS MS F P
Regression 4 5166.9 1291.7 273.89 0.000 Residual Error 16 75.5 4.7
Total 20 5242.4 No replicates. Cannot do pure error test.
Source DF Seq SS Age 1 4448.4
N 1 123.4 D 1 329.6
BA 1 265.5
Regression Analysis: Cstand versus Age, N, BA
The regression equation is Cstand = - 3.04 + 0.916 Age - 0.0105 N + 2.54 BA
Predictor Coef SE Coef T P VIF Constant -3.037 2.050 -1.48 0.157
Age 0.9163 0.4371 2.10 0.051 6.6 N -0.010542 0.002279 -4.63 0.000 2.2
BA 2.5441 0.2298 11.07 0.000 6.8 S = 2.192 R-Sq = 98.4 R-Sqadj = 98.2
PRESS = 144.955 R-Sqpred = 97.23 Analysis of Variance
Source DF SS MS F P Regression 3 5160.7 1720.2 358.14 0.000
Residual Error 17 81.7 4.8 Total 20 5242.4
No replicates. Cannot do pure error test. Source DF Seq SS
Age 1 4448.4 N 1 123.4
BA 1 588.9
Regression Analysis: Cstand versus N, BA
The regression equation is Cstand = - 1.14 - 0.0140 N + 2.99 BA
Predictor Coef SE Coef T P VIF Constant -1.140 2.005 -0.57 0.577
N -0.013986 0.001722 -8.12 0.000 1.1 BA 2.98607 0.09955 30.00 0.000 1.1
S = 2.389 R-Sq = 98.0 R-Sqadj = 97.8 PRESS = 144.827 R-Sqpred = 97.24
Analysis of Variance Source DF SS MS F P
Regression 2 5139.6 2569.8 450.12 0.000 Residual Error 18 102.8 5.7
Total 20 5242.4 No replicates. Cannot do pure error test.
Source DF Seq SS N 1 2.4
BA 1 5137.2 Unusual Observations
Obs N Cstand Fit SE Fit Residual St Resid 14 727 63.430 58.924 1.116 4.506 2.13R
20 950 26.550 32.365 0.523 -5.815 -2.49R R denotes an observation with a large standardized residual
Regression Analysis: Cstand versus BA
The regression equation is Cstand = - 11.3 + 2.78 BA
Predictor Coef SE Coef T P Constant -11.327 3.288 -3.44 0.003
BA 2.7840 0.2026 13.74 0.000 S = 5.023 R-Sq = 90.9 R-Sqadj = 90.4
PRESS = 655.988 R-Sqpred = 87.49 Analysis of Variance
Source DF SS MS F P Regression 1 4763.0 4763.0 188.77 0.000
Residual Error 19 479.4 25.2 Total 20 5242.4
No replicates. Cannot do pure error test. Unusual Observations
Obs BA Cstand Fit SE Fit Residual St Resid 1 2.9 3.87 -3.37 2.75 7.24 1.72 X
3 20.3 34.40 45.16 1.49 -10.76 -2.24R 14 23.5 63.43 54.15 1.99 9.28 2.01R
R denotes an observation with a large standardized residual X denotes an observation whose X value gives it large influence.
Regression Analysis: Cstand versus Age
The regression equation is Cstand = 0.73 + 5.19 Age
Predictor Coef SE Coef T P Constant 0.727 3.279 0.22 0.827
Age 5.1930 0.5033 10.32 0.000 S = 6.465 R-Sq = 84.9 R-Sqadj = 84.1
PRESS = 1000.74 R-Sqpred = 80.91 Analysis of Variance
Source DF SS MS F P Regression 1 4448.4 4448.4 106.44 0.000
Residual Error 19 794.0 41.8 Lack of Fit 4 301.1 75.3 2.29 0.108
Pure Error 15 492.9 32.9 Total 20 5242.4
Unusual Observations Obs Age Cstand Fit SE Fit Residual St Resid
11 7.5 22.05 39.67 1.63 -17.62 -2.82R R denotes an observation with a large standardized residual
Regression Analysis: Clive versus N, D, BA, Age
The regression equation is Clive = 10.2 - 0.0187 N - 0.62 D + 3.32 BA + 0.446 Age
Predictor Coef SE Coef T P VIF Constant 10.23 13.01 0.79 0.443
N -0.018733 0.007173 -2.61 0.019 7.6 D -0.617 1.152 -0.54 0.599 12.6
BA 3.3218 0.6658 4.99 0.000 19.5 Age 0.4460 0.7534 0.59 0.562 6.7
S = 3.737 R-Sq = 96.5 R-Sqadj = 95.7 PRESS = 381.845 R-Sqpred = 94.07
Analysis of Variance Source DF SS MS F P
Regression 4 6218.1 1554.5 111.29 0.000 Residual Error 16 223.5 14.0
Total 20 6441.5 No replicates. Cannot do pure error test.
Source DF Seq SS N 1 21.5
D 1 5489.8 BA 1 701.9
Age 1 4.9 Unusual Observations
Obs N Clive Fit SE Fit Residual St Resid 21 983 23.800 16.743 1.622 7.057 2.10R
R denotes an observation with a large standardized residual
Regression Analysis: Clive versus Age, N, BA
The regression equation is Clive = 3.51 + 0.505 Age - 0.0155 N + 3.03 BA
Predictor Coef SE Coef T P VIF Constant 3.513 3.421 1.03 0.319
Age 0.5047 0.7296 0.69 0.498 6.6 N -0.015501 0.003804 -4.07 0.001 2.2
BA 3.0333 0.3835 7.91 0.000 6.8 S = 3.658 R-Sq = 96.5 R-Sqadj = 95.8
PRESS = 359.627 R-Sqpred = 94.42 Analysis of Variance
Source DF SS MS F P Regression 3 6214.0 2071.3 154.78 0.000
Residual Error 17 227.5 13.4 Total 20 6441.5
No replicates. Cannot do pure error test. Source DF Seq SS
Age 1 5293.7 N 1 83.1
BA 1 837.2 Unusual Observations
Obs Age Clive Fit SE Fit Residual St Resid 21 1.5 23.800 16.878 1.568 6.922 2.09R
R denotes an observation with a large standardized residual
Regression Analysis: Clive versus N, BA
The regression equation is Clive = 4.56 - 0.0174 N + 3.28 BA
Predictor Coef SE Coef T P VIF Constant 4.558 3.025 1.51 0.149
N -0.017399 0.002598 -6.70 0.000 1.1 BA 3.2768 0.1502 21.82 0.000 1.1
S = 3.605 R-Sq = 96.4 R-Sqadj = 96.0 PRESS = 334.018 R-Sqpred = 94.81
Analysis of Variance Source DF SS MS F P
Regression 2 6207.6 3103.8 238.85 0.000 Residual Error 18 233.9 13.0
Total 20 6441.5 No replicates. Cannot do pure error test.
Source DF Seq SS N 1 21.5
BA 1 6186.1
Regression Analysis: Clive versus BA
The regression equation is Clive = - 8.12 + 3.03 BA
Predictor Coef SE Coef T P Constant -8.115 4.292 -1.89 0.074
BA 3.0254 0.2645 11.44 0.000 S = 6.556 R-Sq = 87.3 R-Sqadj = 86.7
PRESS = 1082.04 R-Sqpred = 83.20 Analysis of Variance
Source DF SS MS F P Regression 1 5624.8 5624.8 130.85 0.000
Residual Error 19 816.8 43.0 Total 20 6441.5
No replicates. Cannot do pure error test. Unusual Observations
Obs BA Clive Fit SE Fit Residual St Resid 1 2.9 9.30 0.54 3.59 8.76 1.60 X
X denotes an observation whose X value gives it large influence.
Regression Analysis: Clive versus Age
The regression equation is Clive = 4.86 + 5.67 Age
Predictor Coef SE Coef T P Constant 4.857 3.943 1.23 0.233
Age 5.6650 0.6052 9.36 0.000 S = 7.773 R-Sq = 82.2 R-Sqadj = 81.2
PRESS = 1444.40 R-Sqpred = 77.58 Analysis of Variance
Source DF SS MS F P Regression 1 5293.7 5293.7 87.63 0.000
Residual Error 19 1147.8 60.4 Lack of Fit 4 444.3 111.1 2.37 0.099
Pure Error 15 703.5 46.9 Total 20 6441.5
Unusual Observations Obs Age Clive Fit SE Fit Residual St Resid
11 7.5 27.85 47.34 1.96 -19.49 -2.59R R denotes an observation with a large standardized residual
2. AGROFORESTRI DS KERTAYASA
Descriptive Statistics: CtotAG, Cstandkop, Cstand, Clive, U, N, D, BA
Variable N Mean Median TrMean StDev SE Mean CtotAG 20 40.74 34.11 39.93 17.39 3.89
Cstandk 20 38.16 31.88 37.28 16.84 3.76 Cstand 20 34.48 29.84 33.91 14.84 3.32
Clive 20 38.51 32.27 37.67 16.81 3.76 U 20 6.100 5.500 6.056 2.836 0.634
N 20 1696 1597 1662 488 109 D 20 10.480 10.150 10.467 1.558 0.348
BA 20 18.12 16.56 18.06 6.55 1.47 Variable Minimum Maximum Q1 Q3
CtotAG 9.10 86.95 30.76 54.75 Cstandk 8.49 83.64 28.16 50.57
Cstand 8.49 70.79 25.67 47.97 Clive 8.53 83.67 28.45 50.94
U 1.500 11.500 3.500 7.500 N 1117 2883 1325 1894
D 7.900 13.300 9.425 11.725 BA 7.13 30.19 13.40 23.75
Correlations: CtotAG, Cstandkop, Cstand, Clive, U, N, D, BA
CtotAG Cstandk Cstand Clive U N D Cstandk 0.998
0.000 Cstand 0.985 0.982
0.000 0.000 Clive 0.999 1.000 0.983
0.000 0.000 0.000 U 0.873 0.869 0.849 0.871
0.000 0.000 0.000 0.000 N 0.163 0.157 0.279 0.158 0.060
0.493 0.508 0.233 0.507 0.802 D 0.861 0.852 0.826 0.854 0.832 -0.206
0.000 0.000 0.000 0.000 0.000 0.384 BA 0.890 0.884 0.941 0.885 0.792 0.530 0.685
0.000 0.000 0.000 0.000 0.000 0.016 0.001 Cell Contents: Pearson correlation
P-Value
Best Subsets Regression: CtotAG versus U, N, D, BA
Response is CtotAG B
Vars R-Sq R-Sqadj C-p S U N D A 1 79.3 78.1 26.6 8.1322 X
1 76.3 74.9 32.9 8.7065 X 2 92.6 91.7 1.3 5.0170 X X
2 91.2 90.2 4.1 5.4579 X X 3 92.7 91.3 3.1 5.1366 X X X
3 92.6 91.2 3.2 5.1484 X X X 4 92.7 90.8 5.0 5.2838 X X X X
Regression Analysis: CtotAG versus U, N, D, BA
The regression equation is CtotAG = 2.3 + 0.425 U - 0.0117 N + 0.95 D + 2.53 BA
Predictor Coef SE Coef T P VIF Constant 2.30 27.56 0.08 0.935
U 0.4249 0.9728 0.44 0.668 5.2 N -0.011689 0.007857 -1.49 0.158 10.0
D 0.945 2.716 0.35 0.733 12.2 BA 2.5254 0.8506 2.97 0.010 21.1
S = 5.284 R-Sq = 92.7 R-Sqadj = 90.8 PRESS = 1341.02 R-Sqpred = 76.67
Analysis of Variance Source DF SS MS F P
Regression 4 5330.2 1332.5 47.73 0.000 Residual Error 15 418.8 27.9
Total 19 5748.9 No replicates. Cannot do pure error test.
Source DF Seq SS U 1 4384.5
N 1 70.3 D 1 629.3
BA 1 246.1 Unusual Observations
Obs U CtotAG Fit SE Fit Residual St Resid 13 9.5 58.77 69.64 2.63 -10.87 -2.37R
17 11.5 86.95 78.27 4.30 8.68 2.82R 19 3.5 28.18 18.35 2.33 9.83 2.07R
R denotes an observation with a large standardized residual
Regression Analysis: CtotAG versus U, N, BA
The regression equation is CtotAG = 11.7 + 0.441 U - 0.0140 N + 2.77 BA
Predictor Coef SE Coef T P VIF Constant 11.712 5.142 2.28 0.037
U 0.4405 0.9447 0.47 0.647 5.2 N -0.014029 0.003954 -3.55 0.003 2.7
BA 2.7661 0.4812 5.75 0.000 7.2 S = 5.137 R-Sq = 92.7 R-Sqadj = 91.3
PRESS = 738.612 R-Sqpred = 87.15 Analysis of Variance
Source DF SS MS F P Regression 3 5326.8 1775.6 67.30 0.000
Residual Error 16 422.2 26.4 Total 19 5748.9
No replicates. Cannot do pure error test. Source DF Seq SS
U 1 4384.5 N 1 70.3
BA 1 872.0 Unusual Observations
Obs U CtotAG Fit SE Fit Residual St Resid 13 9.5 58.77 69.52 2.54 -10.75 -2.41R
19 3.5 28.18 18.51 2.22 9.67 2.09R R denotes an observation with a large standardized residual
Regression Analysis: CtotAG versus N, BA
The regression equation is CtotAG = 12.9 - 0.0153 N + 2.97 BA
Predictor Coef SE Coef T P VIF Constant 12.919 4.340 2.98 0.008
N -0.015308 0.002781 -5.50 0.000 1.4 BA 2.9675 0.2071 14.33 0.000 1.4
S = 5.017 R-Sq = 92.6 R-Sqadj = 91.7 PRESS = 689.123 R-Sqpred = 88.01
