Owner's Manual

Table Of Contents
Appendix B: Technical Reference 940
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