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338 Lists&Spreadsheet Application
Quadratic Regression (QuadReg)
Fits the second-degree polynomial y=ax
2
+bx+c to the data. It displays values
for a, b, c, and R
2
. 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.
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.