Appendix B: Technical Reference 918
Regression Description
LnReg Uses the least-squares algorithm and transformed values
ln(
x
) and
y
to fit the model equation:
y
=
a
+
b
ln(
x
)
Logistic Uses the least-squares algorithm to fit the model equation:
y=a/(1+b
*
e
^(c
*
x))+d
MedMed Uses the median-median line (resistant line) technique to
calculate summary points x1, y1, x2, y2, x3, and y3, and
fits the model equation:
y
=
ax
+
b
where
a
is the slope and
b
is the y-intercept.
PowerReg Uses the least-squares algorithm and transformed values
ln(
x
) and ln(
y
) to fit the model equation:
y=ax
b
QuadReg Uses the least-squares algorithm to fit the second-order
polynomial:
y
=
ax
2
+
bx
+
c
For three data points, the equation is a polynomial fit; for
four or more, it is a polynomial regression. At least three
data points are required.
QuartReg Uses the least-squares algorithm to fit the fourth-order
polynomial:
y
=
ax
4
+
bx
3
+
cx
2
+
dx
+
e
For five data points, the equation is a polynomial fit; for six
or more, it is a polynomial regression. At least five data
points are required.
SinReg Uses the least-squares algorithm to fit the model equation:
y=a sin(bx+c)+d
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