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E-126
A =
n
Σy
–
B
.
Σx
–1
B =
Sxx
Sxy
r
=
Sxx
=
Σ
(
x
–1
)
2
–
Syy
=
Σy
2
–
Sxy
=
Σ
(
x
–1
)
y
–
Sxx
.
Syy
Sxy
n
(
Σx
–1
)
2
n
Σx
–1
.
Σy
m
=
y – A
B
n = A +
x
B
n
(
Σy
)
2
Inverse Regression (1/X)
A = exp
(
)
n
Σ
ln
y – B
.
Σ
ln
x
B =
n
.
Σ
(
ln
x
)
2
–
(
Σ
ln
x
)
2
n
.
Σ
ln
x
ln
y
– Σ
ln
x
.
Σ
ln
y
r
=
{
n
.
Σ
(
ln
x
)
2
–
(
Σ
ln
x
)
2
}{
n
.
Σ
(
ln
y
)
2
–
(
Σ
ln
y
)
2
}
n
.
Σ
ln
x
ln
y
– Σ
ln
x
.
Σ
ln
y
m
=
e
B
ln y – ln A
n = Ax
B
Power Regression (A•X^B)