User Manual
6-18
• Linear Regression (
ax + b ) .............
(
a + bx ) .............
• Quadratic Regression .....................
• Cubic Regression ...........................
• Quartic Regression .........................
• Logarithmic Regression ..................
• Exponential Repression (
a · e
bx
) .......
(
a · b
x
) ........
• Power Regression ..........................
• Sin Regression ...............................
• Logistic Regression ........................
u Estimated Value Calculation for Regression Graphs
The STAT mode also includes a Y-CAL function that uses regression to calculate the estimated
y -value for a particular x -value after graphing a paired-variable statistical
regression.
The following is the general procedure for using the Y-CAL function.
1. After drawing a regression graph, press !5(G-SLV) 1(Y-CAL) to enter the graph
selection mode, and then press w.
If there are multiple graphs on the display, use f and c to select the graph you want,
and then press w.
• This causes an
x -value input dialog box to appear.
2. Input the value you want for x and then press w.
• This causes the coordinates for
x and y to appear at
the bottom of the display, and moves the pointer to the
corresponding point on the graph.
M
Se =
Σ
1
n – 2
i=1
n
(y
i
– (ax
i
+ b))
2
M
Se =
Σ
1
n – 2
i=1
n
(y
i
– (ax
i
+ b))
2
M
Se =
Σ
1
n – 2
i=1
n
(yi – (a + bxi))
2
M
Se =
Σ
1
n – 2
i=1
n
(yi – (a + bxi))
2
M
Se =
Σ
1
n – 3
i=1
n
(y
i
– (ax
i
+ bx
i
+ c))
2
2
M
Se =
Σ
1
n – 3
i=1
n
(y
i
– (ax
i
+ bx
i
+ c))
2
2
M
Se =
Σ
1
n – 4
i=1
n
(y
i
– (ax
i
3
+ bx
i
+ cx
i
+ d ))
2
2
M
Se =
Σ
1
n – 4
i=1
n
(y
i
– (ax
i
3
+ bx
i
+ cx
i
+ d ))
2
2
M
Se =
Σ
1
n – 5
i=1
n
(yi – (axi
4
+ bxi
3
+ cxi
+ dxi
+ e))
2
2
M
Se =
Σ
1
n – 5
i=1
n
(yi – (axi
4
+ bxi
3
+ cxi
+ dxi
+ e))
2
2
M
Se =
Σ
1
n – 2
i=1
n
(y
i
– (a + b ln x
i
))
2
M
Se =
Σ
1
n – 2
i=1
n
(y
i
– (a + b ln x
i
))
2
M
Se =
Σ
1
n – 2
i=1
n
(ln y
i
– (ln a + bx
i
))
2
M
Se =
Σ
1
n – 2
i=1
n
(ln y
i
– (ln a + bx
i
))
2
M
Se =
Σ
1
n – 2
i=1
n
(ln yi – (ln a + (ln b) · xi ))
2
M
Se =
Σ
1
n – 2
i=1
n
(ln yi – (ln a + (ln b) · xi ))
2
M
Se =
Σ
1
n – 2
i=1
n
(ln y
i
– (ln a + b ln x
i
))
2
M
Se =
Σ
1
n – 2
i=1
n
(ln y
i
– (ln a + b ln x
i
))
2
M
Se =
Σ
1
n – 2
i=1
n
(yi – (a sin (bxi + c) + d ))
2
M
Se =
Σ
1
n – 2
i=1
n
(yi – (a sin (bxi + c) + d ))
2
M
Se =
Σ
1
n – 2 1 + ae
–bx
i
C
i=1
n
y
i
–
2
M
Se =
Σ
1
n – 2 1 + ae
–bx
i
C
i=1
n
y
i
–
2