Casio fx-FD10 Pro User Manual page 118

• Linear Regression (
• Quadratic Regression .....................
• Cubic Regression ...........................
• Quartic Regression .........................
• Logarithmic Regression ..................
• Exponential Repression (
• 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
Example
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.
The estimated value
ax b
+ ) ............. MSe = MSe =
a bx
MSe = MSe =
( + ).............
MSe = MSe =
MSe = MSe =
MSe = MSe =
MSe = MSe =
bx
a e
· ) ....... MSe = MSe =
x
a b
( · ) ........ MSe = MSe =
MSe = MSe =
MSe = MSe =
MSe = MSe =
To perform a linear regression using the nearby data and estimate the
x
values of and when
xi 10
yi 1003
ca(value of
xi
)
K5(STAT)2( )w
is displayed for
n n
1 1
Σ Σ
(y (y – (ax – (ax
i i
n – 2 n – 2
i=1 i=1
1 1 n n
Σ Σ
(y (y – (a + bx – (a + bx
i i
n – 2 n – 2
i=1 i=1
1 1 n n
Σ Σ
(y (y – (ax – (ax
i i
n – 3 n – 3
i=1 i=1
n n
1 1
Σ Σ
(y (y – (ax – (ax
i i
n – 4 n – 4
i=1 i=1
1 1 n n
Σ Σ
(y (y – (ax – (ax
i i
n – 5 n – 5
i=1 i=1
n n
1 1
Σ Σ
(y (y – (a + b ln x – (a + b ln x
i i
n – 2 n – 2
i=1 i=1
n n
1 1
Σ Σ
(ln y (ln y – (ln a + bx – (ln a + bx
i i
n – 2 n – 2
i=1 i=1
n n
1 1
Σ Σ
(ln y (ln y – (ln a + (ln b) · x – (ln a + (ln b) · x
i i
n – 2 n – 2
i=1 i=1
n n
1 1
Σ Σ
(ln y (ln y – (ln a + b ln x – (ln a + b ln x
i i
n – 2 n – 2
i=1 i=1
n n
1 1
Σ Σ
(y (y – (a sin (bx – (a sin (bx
i i
n – 2 n – 2
i=1 i=1
1 1 n n
Σ Σ
– –
y y
i i
n – 2 n – 2 1 + ae 1 + ae
i=1 i=1
x y
and parameters.
xi yi
= 20 and = 1000
15 20
1005 1010
xi
= 20.
5-22
2 2
+ b)) + b))
i i
2 2
)) ))
i i
2 2
2 2
+ bx + bx + c)) + c))
i i i i
3 3 2 2 2 2
+ bx + bx + cx + cx + d )) + d ))
i i i i i i
4 4 3 3 2 2
+ bx + bx + cx + cx + dx + dx + e)) + e))
i i i i i i i i
2 2
)) ))
i i
2 2
)) ))
i i
2 2
)) ))
i i
2 2
)) ))
i i
2 2
+ c) + d )) + c) + d ))
i i
2 2
C C
–bx –bx
i i
25 30
1011 1014
2 2