Texas Instruments TI-36X Pro User Manual - Page 37

For four points, the equation - linear regression

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3: 2-Var Stats Analyzes paired data from 2 data sets with 2 measured variables-x, the independent variable, and y, the dependent variable. Frequency data may be included. Note: 2-Var Stats also computes a linear regression and populates the linear regression results. 4: LinReg ax+b Fits the model equation y=ax+b to the data using a least-squares fit. It displays values for a (slope) and b (y-intercept); it also displays values for r2 and r. 5: QuadraticReg Fits the second-degree polynomial y=ax2+bx+c to the data. It displays values for a, b, and c; it also displays a value for R2. 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. 6: CubicReg Fits the third-degree polynomial y=ax3+bx2+cx+d to the data. It displays values for a, b, c, and d; it also displays a value for R2. For four points, the equation is a polynomial fit; for five or more, it is a polynomial regression. At least four points are required. 7: LnReg a+blnx Fits the model equation y=a+b ln(x) to the data using a least squares fit and transformed values ln(x) and y. It displays values for a and b; it also displays values for r2 and r. 8: PwrReg ax^b Fits the model equation y=axb to the data using a least-squares fit and transformed values ln(x) and ln(y). It displays values for a and b; it also displays values for r2 and r. 9: ExpReg ab^x Fits the model equation y=abx to the data using a least-squares fit and transformed values x and ln(y). It displays values for a and b; it also displays values for r2 and r. 37

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37
3: 2-Var Stats
Analyzes paired data from 2 data sets with
2 measured variables—
x
, the independent
variable, and
y
, the dependent variable.
Frequency data may be included.
Note:
2-Var Stats also computes a linear
regression and populates the linear
regression results.
4: LinReg a
x
+b
Fits the model equation y=ax+b to the data
using a least-squares fit. It displays values
for
a
(slope) and
b
(y-intercept); it also
displays values for
r
2
and
r
.
5: QuadraticReg
Fits the second-degree polynomial
y=ax
2
+bx+c to the data. It displays values
for
a
,
b
, and
c
; it also displays a value for
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.
6: CubicReg
Fits the third-degree polynomial
y=ax
3
+bx
2
+cx+d to the data. It displays
values for
a
,
b
,
c
, and
d
; it also displays a
value for
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.
7: LnReg a+bln
x
Fits the model equation y=a+b ln(x) to the
data using a least squares fit and
transformed values ln(x) and y. It displays
values for
a
and
b
; it also displays values
for
r
2
and
r
.
8: PwrReg a
x
^b
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
and
b
; it also displays values for
r
2
and
r
.
9: ExpReg ab^
x
Fits the model equation y=ab
x
to the data
using a least-squares fit and transformed
values x and ln(y). It displays values for
a
and
b
; it also displays values for
r
2
and
r
.