Texas Instruments TI-NSPIRE Software Manual page 230

Each element in
List1,List2
( ).
Linear Regression (mx+b) (LinRegMx)
The
Linear Regression (mx+b)
using a least-squares fit. It displays values for
Linear Regression (a+bx) (LinRegBx)
The
Linear Regression (a+bx)
using a least-squares fit. It displays values for
and .
r
Median-Median Line Regression (MedMed)
The
Median-Median Line
the data using the median-median line (resistant line) technique,
calculating the summary points x1, y1, x2, y2, x3, and y3.
Line displays values for
Quadratic Regression (QuadReg)
The (quadratic regression) fits the second-degree polynomial
QuadReg
2
y=ax +bx+c to the data. It displays values for
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)
The
CubicRegregression
3 2
y=ax +bx +cx+d to the data. It displays values 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)
The
Quartic Regression
4 3 2
y=ax +bx +cx +dx+e to the data. It displays values for
2
. For five points, the equation is a polynomial fit; for six or more, it is a
R
polynomial regression. At least five points are required.
Power Regression (PwrReg)
The
Power Regression
least-squares fit and transformed values ln(x) and ln(y). It displays values
2
for , , , and
a b r
218
freqlist
is the frequency of occurrence for each data pair
fits the model equation y=ax+b to the data
fits the model equation y=a+bx to the data
regression fits the model equation y=ax+b to
(slope) and
a
fits the third-degree polynomial
fits the fourth-degree polynomial
fits the model equation y=ax b to the data using a
.
r
(slope) and
m
(y-intercept),
a
(y-intercept).
b
, ,
a b c, and R
,
a b
Using Lists & Spreadsheet
(y-intercept).
b
(slope),
b r
Median-Median
2
. For three
2
, , . For
c d, and R
, , , ,
a b c d e, and
2
,