Agriculture Reference
In-Depth Information
gets a residual of 0.40, then it is quite clear
that only 60% variation in the response vari-
able can be explained by the variables/
characters under consideration and 40% vari-
ation in the response variable/character will
remain unexplained. Are we satisfied? It is
very difficult to answer such. Actually the
decision norm is governed by several factors
like the type of experiment and material being
handled and the situation of the experimenta-
tion. In agronomic field trials, one may be
satisfied with 0.30 residual value; in labora-
tory experiment, one may be satisfied with a
residual less than 0.10; on the other hand, in
medical research, one may be satisfied with a
residual less than 0.001 or so.
2. When the path coefficient value, that is, the
direct effect of the causal character/variable,
is almost equal to that of the correlation
coefficients between them, the correlation
coefficient presents the true picture of linear
association between the response variable/
character and the independent causal character.
3. When the direct effect is negligible or nega-
tive but the correlation coefficient between the
causal variable/character is positive and sig-
nificant, then the indirect effects are the cause
of such correlation coefficients. In this situa-
tion, it is better to consider the other causal
variables/characters rather than this variable/
character.
4. When the direct effect is high and positive but
the correlation coefficient is negative or neg-
ligible, then the indirect effects are the cause
of such correlation. It is better to restrict the
undesirable characters.
5. When both the direct effect and the correla-
tion coefficient are negligible or negative,
then one should discard such characters or
variables.
Example 11.3.
The following data give the yield
(
Y
) corresponding to four yield components (
X
1
,
X
2
,
X
4
) for a certain agricultural crop. Work
out the path of correlations of yield with the yield
components.
X
3
,
Y
X
1
X
2
X
3
X
4
Y
X
1
X
2
X
3
X
4
99.56
6.60
4.07
8.20
8.20
121.87
6.40
6.62
7.40
8.60
89.23
7.20
3.82
4.80
5.20
102.06
6.20
6.52
6.60
7.20
98.76
8.00
3.63
7.20
7.40
119.68
6.60
6.01
10.20
6.60
136.33
4.20
5.83
8.80
7.40
133.12
5.80
6.22
9.40
8.00
153.12
5.20
5.42
8.20
7.20
129.70
6.20
6.14
9.60
6.80
135.05
4.40
5.62
10.20
6.20
64.84
3.20
5.73
8.20
5.20
93.86
6.20
6.44
6.20
7.20
65.37
3.20
5.04
7.80
4.40
98.08
5.80
5.40
8.60
5.40
61.28
2.80
5.40
9.20
5.20
102.06
5.80
5.50
6.60
5.20
81.32
2.80
7.08
7.20
5.40
82.31
4.20
6.90
8.20
4.00
73.59
2.60
6.32
10.20
5.00
65.37
4.60
6.69
7.40
6.60
71.89
3.60
6.27
7.40
7.40
69.37
4.40
7.12
9.20
5.00
58.33
2.80
7.08
9.20
8.20
69.17
3.60
5.78
9.40
6.00
60.25
3.00
6.43
8.40
6.20
59.24
3.20
5.35
8.60
6.20
49.72
3.20
6.20
6.80
5.20
63.59
3.20
5.45
8.80
4.40
80.94
3.40
6.51
8.40
4.20
61.11
4.20
5.97
8.20
7.20
78.64
2.60
8.32
9.20
4.00
59.69
3.80
7.32
7.40
6.20
83.21
3.00
7.29
6.60
3.60
53.20
4.00
7.19
6.80
5.80
57.38
2.80
5.88
9.60
4.40
83.53
5.20
5.49
10.20
4.40
60.12
2.80
5.38
8.20
4.20
79.11
5.40
4.72
8.40
5.20
61.09
3.40
5.49
9.20
3.20
70.13
4.60
4.82
7.60
4.20
49.86
3.80
7.04
8.80
5.20
129.21
8.00
3.12
9.20
5.20
57.15
3.20
6.88
7.40
3.40
(continued)
