Normal Linear Regression
Dependent variable: Y
Independent variables:
A T S-Ne C P E Ax(S-Ne) AxC AxP AxE
Student t(13) Prob.
Y = -3.49916 + 16.24534 * A 2.94 0.0114
- 0.04497 * T 1.80 0.0959
+ 0.41983 * S-Ne 0.50 0.6274
+ 0.38786 * C 1.78 0.0984
+ 4.10840 * P 1.14 0.2745
+ 3.15286 * E 1.63 0.1268
- 3.19722 * Ax(S-Ne) 2.52 0.0254
- 0.48625 * AxC 2.02 0.0648
- 2.55715 * AxP 0.57 0.5779
- 0.56229 * AxE 0.21 0.8349
Variance = 4.860685
Observed Fitted Residual
1 12.85000 9.86429 2.98571
2 5.52000 6.02917 -0.50917
3 6.29000 6.32853 -0.03853
4 6.11000 4.67708 1.43292
5 2.45000 2.95223 -0.50223
6 3.61000 5.82370 -2.21370
7 0.47000 2.43537 -1.96537
8 4.56000 2.63331 1.92669
9 6.35000 7.41442 -1.06442
10 5.06000 7.58588 -2.52588
11 2.76000 1.77467 0.98533
12 4.05000 5.34414 -1.29414
13 5.74000 5.73560 0.00440
14 4.84000 5.60540 -0.76540
15 11.86000 7.46871 4.39129
16 4.45000 5.59698 -1.14698
17 3.66000 4.43204 -0.77204
18 4.22000 3.75133 0.46867
19 1.16000 0.52870 0.63130
20 5.45000 5.41144 0.03856
21 2.02000 3.47287 -1.45287
22 0.82000 -0.51492 1.33492
23 1.09000 -0.11433 1.20433
24 0.28000 1.43340 -1.15340
One-Way Analysis of Variance
Dependent variable: Y; Independent variable: A
Category 1 2
Mean 4.1717 4.6342
S.D. 3.5192 2.7768
Number 12 12
SS MSS d.f. F Prob.
Overall effect 222.3349 9.6667 23
A 1.2834 1.2834 1 0.13 0.7242
Residual 221.0515 10.0478 22
Analysis of Covariance
Dependent variable: Y; Block variable: A
Covariates:
T
Block 1 with 12 observations and mean of Y = 4.1717
Total SS = 136.2322; Residual SS = 74.5963
Regression model for block 1
Student t(10) Prob. Mean
Y = 7.30868- 0.09071*T 2.87 0.0165 34.5833
Block 2 with 12 observations and mean of Y = 4.6342
Total SS = 84.8193; Residual SS = 77.9834
Regression model for block 2
Student t(10) Prob. Mean
Y = 5.74639- 0.03082*T 0.94 0.3712 36.0833
Regression model with parallel lines
Intercepts
a(1) = 6.29404
a(2) = 6.84859
Slopes
Student t(21) Prob. Mean
- 0.06137*T 2.65 0.0151 35.3333
Regression model with all blocks combined
Student t(22) Prob. Mean
Y = 6.55932- 0.06103*T 2.68 0.0136 35.3333
Analysis of Variance Table
SS MSS d.f. F Prob
Total 222.33
a's unequal, b(i)=0 55.31 55.31 1 7.01 0.0151
a's equal, b(i)'s equal 1.84 1.84 1 0.23 0.6339
Residual, b(i)'s equal 165.74 7.89 21
a's unequal, b(i)'s equal 13.16 13.16 1 1.73 0.2039
a's equal, b(i)'s unequal 3.67 3.67 1 0.48 0.4958
Residual, b(i)'s unequal 152.58 7.63 20
Analysis of Covariance
Dependent variable: Y; Block variable: A
Covariates:
TS-Ne
Block 1 with 12 observations and mean of Y = 4.1717
Total SS = 136.2322; Residual SS = 60.0609
Regression model for block 1
Student t(9) Prob. Mean
Y = 4.01815- 0.14484*T 3.06 0.0135 34.5833
+ 1.71610*S-Ne 1.48 0.1741 3.0083
Block 2 with 12 observations and mean of Y = 4.6342
Total SS = 84.8193; Residual SS = 45.2174
Regression model for block 2
Student t(9) Prob. Mean
Y = 13.02809- 0.02958*T 1.12 0.2921 36.0833
- 2.20022*S-Ne 2.55 0.0310 3.3300
Regression model with parallel lines
Intercepts
a(1) = 8.41621
a(2) = 9.22894
Slopes
Student t(20) Prob. Mean
- 0.04715*T 1.83 0.0816 35.3333
- 0.86893*S-Ne 1.22 0.2363 3.1692
Regression model with all blocks combined
Student t(21) Prob. Mean
Y = 8.56754- 0.04817*T 1.90 0.0713 35.3333
- 0.77705*S-Ne 1.12 0.2736 3.1692
Analysis of Variance Table
SS MSS d.f. F Prob
Total 222.33
a's unequal, b(i)=0 66.81 33.40 2 4.33 0.0274
a's equal, b(i)'s equal 3.83 3.83 1 0.50 0.4893
Residual, b(i)'s equal 154.25 7.71 20
a's unequal, b(i)'s equal 48.97 24.48 2 4.19 0.0321
a's equal, b(i)'s unequal 2.61 2.61 1 0.45 0.5128
Residual, b(i)'s unequal 105.28 5.85 18
Analysis of Covariance
Dependent variable: Y; Block variable: A
Covariates:
TS-NeC
Block 1 with 12 observations and mean of Y = 4.1717
Total SS = 136.2322; Residual SS = 37.4032
Regression model for block 1
Student t(8) Prob. Mean
Y = 1.52230- 0.09756*T 2.17 0.0622 34.5833
+ 0.99007*S-Ne 0.96 0.3635 3.0083
+ 0.44724*C 2.20 0.0589 6.8083
Block 2 with 12 observations and mean of Y = 4.6342
Total SS = 84.8193; Residual SS = 43.2418
Regression model for block 2
Student t(8) Prob. Mean
Y = 14.12964- 0.03673*T 1.23 0.2536 36.0833
- 2.34039*S-Ne 2.54 0.0350 3.3300
- 0.04900*C 0.60 0.5622 7.6833
Regression model with parallel lines
Intercepts
a(1) = 7.95924
a(2) = 8.72624
Slopes
Student t(19) Prob. Mean
- 0.04278*T 1.55 0.1379 35.3333
- 0.86674*S-Ne 1.19 0.2469 3.1692
+ 0.04397*C 0.50 0.6228 7.2458
Regression model with all blocks combined
Student t(20) Prob. Mean
Y = 8.05513- 0.04330*T 1.59 0.1272 35.3333
- 0.78034*S-Ne 1.11 0.2800 3.1692
+ 0.04840*C 0.56 0.5816 7.2458
Analysis of Variance Table
SS MSS d.f. F Prob
Total 222.33
a's unequal, b(i)=0 68.81 22.94 3 2.86 0.0640
a's equal, b(i)'s equal 3.39 3.39 1 0.42 0.5233
Residual, b(i)'s equal 152.24 8.01 19
a's unequal, b(i)'s equal 71.60 23.87 3 4.73 0.0150
a's equal, b(i)'s unequal 1.43 1.43 1 0.28 0.6020
Residual, b(i)'s unequal 80.65 5.04 16
Analysis of Covariance
Dependent variable: Y; Block variable: A
Covariates:
TS-NeCP
Block 1 with 12 observations and mean of Y = 4.1717
Total SS = 136.2322; Residual SS = 37.3221
Regression model for block 1
Student t(7) Prob. Mean
Y = 1.52004- 0.10061*T 1.86 0.1050 34.5833
+ 1.06447*S-Ne 0.85 0.4234 3.0083
+ 0.45188*C 2.05 0.0793 6.8083
- 0.43950*P 0.12 0.9053 0.3362
Block 2 with 12 observations and mean of Y = 4.6342
Total SS = 84.8193; Residual SS = 42.8057
Regression model for block 2
Student t(7) Prob. Mean
Y = 14.25294- 0.03667*T 1.15 0.2861 36.0833
- 2.39825*S-Ne 2.38 0.0485 3.3300
- 0.07295*C 0.59 0.5760 7.6833
+ 0.79021*P 0.27 0.7971 0.3179
Regression model with parallel lines
Intercepts
a(1) = 7.96614
a(2) = 8.76653
Slopes
Student t(18) Prob. Mean
- 0.04187*T 1.46 0.1621 35.3333
- 0.90525*S-Ne 1.18 0.2547 3.1692
+ 0.02941*C 0.25 0.8033 7.2458
+ 0.52510*P 0.20 0.8450 0.3271
Regression model with all blocks combined
Student t(19) Prob. Mean
Y = 8.06117- 0.04281*T 1.52 0.1460 35.3333
- 0.79928*S-Ne 1.08 0.2941 3.1692
+ 0.04057*C 0.36 0.7243 7.2458
+ 0.28623*P 0.11 0.9129 0.3271
Analysis of Variance Table
SS MSS d.f. F Prob
Total 222.33
a's unequal, b(i)=0 69.14 17.29 4 2.05 0.1304
a's equal, b(i)'s equal 3.62 3.62 1 0.43 0.5209
Residual, b(i)'s equal 151.91 8.44 18
a's unequal, b(i)'s equal 71.78 17.95 4 3.14 0.0489
a's equal, b(i)'s unequal 1.12 1.12 1 0.20 0.6653
Residual, b(i)'s unequal 80.13 5.72 14
Analysis of Covariance
Dependent variable: Y; Block variable: A
Covariates:
TS-NeCPE
Block 1 with 12 observations and mean of Y = 4.1717
Total SS = 136.2322; Residual SS = 25.9705
Regression model for block 1
Student t(6) Prob. Mean
Y = -3.40341- 0.09222*T 1.88 0.1087 34.5833
+ 1.31606*S-Ne 1.16 0.2918 3.0083
+ 0.33076*C 1.56 0.1698 6.8083
+ 2.51569*P 0.68 0.5211 0.3362
+ 2.96603*E 1.62 0.1565 1.2500
Block 2 with 12 observations and mean of Y = 4.6342
Total SS = 84.8193; Residual SS = 31.9668
Regression model for block 2
Student t(6) Prob. Mean
Y = 11.89099- 0.03062*T 1.02 0.3460 36.0833
- 2.75907*S-Ne 2.84 0.0296 3.3300
- 0.08406*C 0.72 0.4972 7.6833
+ 1.59889*P 0.57 0.5913 0.3179
+ 2.72007*E 1.43 0.2037 1.1667
Regression model with parallel lines
Intercepts
a(1) = 4.65437
a(2) = 5.66757
Slopes
Student t(17) Prob. Mean
- 0.03212*T 1.11 0.2821 35.3333
- 0.91086*S-Ne 1.21 0.2419 3.1692
+ 0.00565*C 0.05 0.9613 7.2458
+ 1.84379*P 0.67 0.5128 0.3271
+ 2.16799*E 1.37 0.1877 1.2083
Regression model with all blocks combined
Student t(18) Prob. Mean
Y = 5.03436- 0.03407*T 1.19 0.2489 35.3333
- 0.77849*S-Ne 1.07 0.2994 3.1692
+ 0.02143*C 0.19 0.8510 7.2458
+ 1.44223*P 0.54 0.5990 0.3271
+ 1.99667*E 1.29 0.2150 1.2083
Analysis of Variance Table
SS MSS d.f. F Prob
Total 222.33
a's unequal, b(i)=0 84.30 16.86 5 2.10 0.1159
a's equal, b(i)'s equal 5.70 5.70 1 0.71 0.4115
Residual, b(i)'s equal 136.75 8.04 17
a's unequal, b(i)'s equal 78.81 15.76 5 3.26 0.0432
a's equal, b(i)'s unequal 2.13 2.13 1 0.44 0.5188
Residual, b(i)'s unequal 57.94 4.83 12