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u Correlation coefficient
r
u Regression coefficient A
A = exp
(
)
n
Σ
ln
y – B
.
Σx
u Regression coefficient B
B =
n
.
Σx
2
–
(
Σx
)
2
n
.
Σx
ln
y
– Σx
.
Σ
ln
y
r
=
{
n
.
Σx
2
–
(
Σx
)
2
}{
n
.
Σ
(
ln
y
)
2
–
(
Σ
ln
y
)
2
}
n
.
Σx
ln
y
– Σx
.
Σ
ln
y
u Correlation coefficient
r
u Regression coefficient A
A = exp
(
)
n
Σ
ln
y – B
.
Σ
ln
x
u Regression coefficient B
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
u Correlation coefficient
r
u Regression coefficient A
A =
n
Σy – B
.
Σx
u Regression coefficient B
B =
n
.
Σx
2
–
(
Σx
)
2
n
.
Σxy – Σx
.
Σy
r
=
{
n
.
Σx
2
–
(
Σx
)
2
}{
n
.
Σy
2
–
(
Σy
)
2
}
n
.
Σxy – Σx
.
Σy
u Correlation coefficient
r
u Regression coefficient A
A =
n
Σy – B
.
Σ
ln
x
u Regression coefficient B
B =
n
.
Σ
(
ln
x
)
2
–
(
Σ
ln
x
)
2
n
.
Σ
(
ln
x
)
y
– Σ
ln
x
.
Σy
r
=
{
n
.
Σ
(
ln
x
)
2
–
(
Σ
ln
x
)
2
}{
n
.
Σy
2
–
(
Σy
)
2
}
n
.
Σ
(
ln
x
)
y
– Σ
ln
x
.
Σy
2 Logarithmic Regression y = A + B
.
ln x
1 Linear Regression y = A + Bx
3 Exponential Regression y = A
.
e
B
·
x
(ln y = ln A + Bx)
4 Power Regression y = A
.
x
B
(ln y = ln A + Bln x)