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Appendix B: Reference Information 571
8992APPB DOC TI
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89/TI
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92 Plus:8992a
pp
b doc (English) SusanGullord Revised:02/23/01 1:54 PM Printed: 02/23/01 2:24 PM Page 571 of 34
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