Texas Instruments PLUS TI-89 - Manual (Page 588)

Appendix B: Reference Information 571
8992APPB DOC TI-89/TI-92 Plus:8992appb doc (English) Susan Gullord 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