![](data:image/png;base64,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)
–15–
1Nc3(STAT)2(OFF)
N2(STAT)
2(A+BX)1=
STAT
1.2= 1.5=
1.6= 1.9=
2.1= 2.4=
STAT
2.5= 2.7=
3=
ce1=
STAT
m
y – A
B
=
r
=
{
n
.
Σx
2
–
(
Σx
)
2
}{
n
.
Σy
2
–
(
Σy
)
2
}
n
.
Σxy – Σx
.
Σy
n = A + Bx
xy
1.0 1.0
1.2 1.1
1.5 1.2
1.6 1.3
1.9 1.4
xy
2.1 1.5
2.4 1.6
2.5 1.7
2.7 1.8
3.0 2.0
#047