Texas Instruments TI-Nspire - Page 230

218 Using Lists & Spreadsheet
Each element in freqlist is the frequency of occurrence for each data pair
(
List1,List2).
Linear Regression (mx+b) (LinRegMx)
The Linear Regression (mx+b) fits the model equation y=ax+b to the data
using a least-squares fit. It displays values for m (slope) and b (y-intercept).
Linear Regression (a+bx) (LinRegBx)
The Linear Regression (a+bx) fits the model equation y=a+bx to the data
using a least-squares fit. It displays values for
a (y-intercept), b (slope), r
2
,
and r.
Median-Median Line Regression (MedMed)
The Median-Median Line regression fits the model equation y=ax+b to
the data using the median-median line (resistant line) technique,
calculating the summary points x1, y1, x2, y2, x3, and y3.
Median-Median
Line displays values for a (slope) and b (y-intercept).
Quadratic Regression (QuadReg)
The QuadReg (quadratic regression) 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)
The CubicRegregression 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)
The Quartic Regression 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 (PwrReg)
The Power Regression fits the model equation y=ax
b
to the data using a
least-squares fit and transformed values ln(x) and ln(y). It displays values
for
a, b, r
2
, and r.