Casio CFX-9800G-w Owners Manual - Page 75

scoci

Page 75 highlights

•Exponential Regression •The exponential regression formula is y=Atet , (Iny=lnA+Bx). •Ey is obtained as Elny, Eye as E(Iny)2, and Exy as Extlny. Example Operation Display xi 6.9 12.9 . 19.8 26.7 35.1 yi 21:4 15.7 12.1 8.5 5.2 ' The data in the above table can be used to obtain, the terms of the regression formula and the correlation coefficient. Based on the regression formula, estimated value ft can be obtained for xi= 16, and estimated value 2 can be obtained for yi= 20. E_(SET)E(EXP) (NON) EMI EDE gaScfig ' (Clears memory) 6.9E( ,)21.4E(DT) 12.9E(,)15.7E(DT), 19.8E( ,)12.1E(DT) 26.7a,)8.5ID (DT) 35.1E(,)5.2E(IT0 (Constani term A) - El(REG)EKNEI (Regression coefficient B) D(B)El (Correlation coefficient r) El(r)g hen xi= 16) 16 Ps (2 hen Yi=20) 20g( 2.)E1 6.9 12.9 19.8 26.7 35.1 ' =30.49758743 - 0.04920370831 - 0.997247352 13.87915739 8.574868047 liPower Regression •The power regression formula is y-Fox6 InA + Blnx). •Ex is obtained as Elnx, Ex2 as E(Inx)2, Ey as £Iny, Ey2 as E(Iny) , and Exy as Elnxdny. Example Operation Display xi YI 28 2410 30 3033 33 3895 35 4491 38 5717 The data in the above table can be used to obtain the terms of the regression for'mule and the correlation coefficient. Based on the regression formula, estimated value 9 can be obtained for xi=40, and estimated value -2 can be obtained for yi= 1000. gETE)TE)fFf4PWR) EDI gm(scoci (Clears memory) 28E1(,)2410E(DT) 30E(,)10331D(DT) 33E (,)3895E(DT) 35E1(,)4491E(DT) 38ID( ,)5717E(DT) (Constant term A) El(REG)Elfit0E) (Regression coefficient a) F2 (B) (Correlation coefficient r) El(r)CI (9 when xi= 40) 40El(fi) :l (2 when yi= 1000) 1000 re 3.33220451 3.401197382 3.496507561 3.555348061 3.63758616 0.2388010685 2.771866158 0.9989062551 6587.674589 20.26225681 -114 - - 115- A340087-13

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•Exponential
Regression
•The
exponential
regression
formula
is
y=Atet
,
(Iny=lnA+Bx).
•Ey
is
obtained
as
Elny,
Eye
as
E(Iny)
2
,
and
Exy
as
Extlny.
Example
Operation
Display
xi
yi
6.9
21:4
12.9
15.7
-
.
19.8
12.1
26.7
8.5
35.1
5.2
'
The
data
in
the
above
table
can
be
used
to
obtain,
the
terms
of
the
regression
for-
mula
and
the
correlation
coefficient.
Based
on
the
regression
formula,
estimat-
ed
value
ft
can
be
obtained
for
xi=
16,
and
estimated
value
2
can
be
obtained
for
yi=
20.
E_(SET)E(EXP)
(NON)
gaScfig
'
(Clears
memory)
6.9E(
,)21.4E(DT)
12.9E(,)15.7E(DT),
19.8E(
,)12.1E(DT)
26.7a,)8.5ID
(DT)
35.1E(,)5.2E(IT0
(Constani
term
A)
-
El(REG)EKNEI
(2
EDE
EMI
(Regression
coefficient
B)
D(B)El
(Correlation
coefficient
r)
El(r)g
hen
xi=
16)
16
Ps
hen
Yi=20)
20g(
2
.)E1
6.9
12.9
19.8
26.7
35.1
'
=30.49758743
-
0.04920370831
-
0.997247352
13.87915739
8.574868047
liPower
Regression
•The
power
regression
formula
is
y-Fox
6
InA
+
Blnx).
•Ex
is
obtained
as
Elnx,
Ex
2
as
E(Inx)
2
,
Ey
as
£Iny,
Ey
2
as
E(Iny)
,
and
Exy
as
Elnxdny.
Example
Operation
Display
xi
YI
28
2410
30
3033
33
3895
35
4491
38
5717
The
data
in
the
above
table
can
be
used
to
obtain
the
terms
of
the
regression
for
-
'mule
and
the
correlation
coefficient.
Based
on
the
regression
formula,
estimated
value
9
can
be
obtained
for
xi=40,
and
estimated
value
-2
can
be
obtained
for
yi=
1000.
gET)EffPWR)
ET)
F4
gm
(
scoci
(Clears
memory)
28E1(,)2410E(DT)
30E(
,)
10331D(DT)
33E
(,)3895E(DT)
35E1(,)4491E(DT)
38ID(
,)5717E(DT)
(Constant
term
A)
El(REG)Elfit0E)
(Regression
coefficient
a)
EDI
F2
(B)
(Correlation
coefficient
r)
El(r)CI
(9
when
xi=
40)
40El(fi) :l
(2
when
yi=
1000)
1000
re
3.33220451
3.401197382
3.496507561
3.555348061
3.63758616
0.2388010685
2.771866158
0.9989062551
6587.674589
20.26225681
-114
-
-
115-
A340087-13