Application Guide

Cubic Regression (CubicReg)
Fits the third-degree polynomial y=ax
3
+bx
2
+cx+d to the data. It displays values for a, b,
c, d, and R
2
. For four points, the equation is a polynomial fit; for five or more, it is a
polynomial regression. At least four points are required.
Quartic Regression (QuartReg)
Fits the fourth-degree polynomial y=ax
4
+bx
3
+cx
2
+dx+e to the data. It displays values
for a, b, c, d, e, and R
2
. For five points, the equation is a polynomial fit; for six or more,
it is a polynomial regression. At least five points are required.
Power Regression (PowerReg)
Fits the model equation y=axb to the data using a least-squares fit on transformed
values ln(x) and ln(y). It displays values for a, b, r
2
, and r.
Exponential Regression (ExpReg)
Fits the model equation y=ab
x
to the data using a least-squares fit on transformed
values x and ln(y). It displays values for a, b, r
2
, and r.
Logarithmic Regression (LogReg)
Fits the model equation y=a+bln(x) to the data using a least-squares fit on
transformed values ln(x) and y. It displays values for a, b, r
2
, and r.
Sinusoidal Regression (SinReg)
Fits the model equation y=asin(bx+c)+d to the data using an iterative least-squares fit.
It displays values for a, b, c, and d. At least four data points are required. At least two
data points per cycle are required to avoid aliased frequency estimates.
Note: The output of SinReg is always in radians, regardless of the Radian/Degree mode
setting.
Logistic Regression (d=0) (Logistic)
Fits the model equation y=c/(1+a*e⁻bx) to the data using an iterative least-squares fit.
It displays values for a, b, and c.
Logistic Regression (dƒ0) (LogisticD)
Fits the model equation y=c(1+a*e
(
⁻bx
)
)+d to the data using an iterative least-squares
fit. It displays values for a, b, c and d.
Multiple Linear Regression (MultReg)
Calculates multiple linear regression of list Y on lists X1, X2, …, X10.
Lists&Spreadsheet Application 329