Texas Instruments TI-89, TI-92

Appendix B: Reference Information 571

8992APPBDOC TI

89/TI

92Plus:8992a

bdoc (English) SusanGullord Revised:02/23/01 1:54 PM Printed:02/23/01 2:24 PM Page571 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=axb

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