Texas Instruments TITANIUM TI-89 - Manual (Page 944)

Appendix B: Technical Reference 945
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 = 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.
QuartReg Uses the least-squares algorithm to fit the fourth-
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.
SinReg Uses the least-squares algorithm to fit the model
equation:
y=a sin(bx+c)+d