Texas Instruments TI-89 사용자 설명서
Appendix B: Technical Reference
945
LnReg
Uses the least-squares algorithm and transformed
values ln(
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:
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:
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(
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:
order polynomial:
y
=
ax2
+
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.
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:
order polynomial:
y
=
ax4
+
bx3
+
cx2
+
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.
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:
equation:
y=a sin(bx+c)+d