A=exp(51yc551iit – HP SmartCalc 300s User Manual
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■ Sum sub-menu(sums),Var sub-menu(number of
samples,mean, standard deviatian),and MinMax
sub-menu(maximum value,minmum value)
operations are the same those for linear regression
calculations.
Quadratic Regression Calculation:
• For examples:
All the data be used as follow table:
X
y
X
y
1.0
1.0
2.1
1.5
1.2
1.1
2.4
1.6
1.5
1.2
2.5
1.7
1.6
1.3
2 . 7
1.8
1.9
1.4
3.0
2.0
sK_+cx^:
1:1-VAR, 2:A-rBX
3: -rCX^ 4:lnX ^
5:^'X^ 6:A-B^X
7:A-X^B 8:1/X
STAT
1 ^ 1
' 1
1
2 1.2
3 1.5
i:2 1
STAT
B
0
1:A
3:C
55i2
2:B
4:5^1
6:y
0.7028598638
ISHIFT
im(STAT)
H(Reg) B
E](B)S
0.2576384379
STAT B
ISHIFT
im(STAT)
H(Reg) C
)H 0.05610274153
y=3^-xi=?
_____ I
sta
T
rslISHiFT
im(STAT)
0(Reg)
3ih
3](xi)H
4.502211457
y= 3-^X2=?
______ I
STAT
B
I
i3llSHIFT
imfSTAT) [7](Reg)
3ib
S](^)S
-9.094472563
x=2-^y=?
STAT
B
I
rSIISHIFT
im(STAT) [7](Reg)
2
y
y)S
1.442547706
Comments for Other Types of Regression
For details about the calculation formula of the
command included in each regression type, refer to
the indicated calculation formulas
For example:
Logarithmic Regression (In X)
y=A+BlnX
A Ey-B-EInx
75— n
p n-Eilnxly-EInxEy
“ n-E(lnx)2-(Elnx)5
V —
n-E(lnx)y^ElnxEy
7{n-E(lnxy-(Elnx)2}{n-E)7-(Eyn
A -fci
x=e ®
y-A+Blnx
e Exponential Regression (e" X)
y=Ae“
A=exp(5iny_B^^
D _ xl n y-E x*£ I ny
n£x2-(Ex)2
n*Exlny-ExElny
V{n-Ex2-(i:x)2^{n-i:(|ny)2-(Elny)2}
A lny-ln>A
^=Ae^
ab Exponential Regression (A ■ B~X)
y=AB"
A= ex
B=exp(TSl|n^|^)
nExIny-ExElny
7{n-Ex2-(Ex)2}{n-E(lny)2-{Slny)2}
-----------------------------------SS----------------------------------- '
_lny-
In
InA
y=AET
Power Regression(A • X B)
y^AX'
A=exp(51yc551iit^
D nEinxInv-Einx'Elnv
n.E(lnxP-(Elnx)2
n-EinxIny-EinxElny
y{n-E(lnx)2(Elnx}^{n.E(lny)2-(Elny)2}
. Iny—InA
x=e^
^=Ax‘
Inverse Regression (1/X)
y=A4
A Tly-BEx’
f- Sxy
VSxxSyy
Sxx=E(x'p-
(Ex')2
Syy=Ey2-%i'
Sxy=E(x')y-^4^
6__ B_
y-A
9=A+i
Comparison Regression Curves
The following example uses the data input in as
following table:
X
y
X
y
1.0
1.0
2.1
1.5
1.2
1.1
2.4
1.6
1.5
1.2
2.5
1.7
1.6
1.3
2.7
1.8
1.9
1.4
3.0
2.0
Compare the correlation coefficient for logarithmic,
e exponential,
ab
exponential. Power, and inverse
regression.
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