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• 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 ........................
k Estimated Value Calculation ( , )
After drawing a regression graph with the STAT mode, you can use the RUN • MAT mode to
calculate estimated values for the regression graph’s x and y parameters.
Example To perform a linear regression using the nearby data and estimate the
values of
ţ and ů x when xi = 20 and yi = 1000
xi
10 15 20 25 30
yi
1003 1005 1010 1011 1014
1. From the Main Menu, enter the STAT mode.
2. Input data into the list and draw the linear regression graph.
3. From the Main Menu, enter the RUN • MAT mode.
4. Press the keys as follows.
ca(value of
xi)
K5(STAT)2(
ţ)w
The estimated value
ţ is displayed for xi = 20.
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
(y
i
– (ax
i
4
+ bx
i
3
+ cx
i
+ dx
i
+ e))
2
2
M
Se =
Σ
1
n – 5
i=1
n
(y
i
– (ax
i
4
+ bx
i
3
+ cx
i
+ dx
i
+ 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