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d
n
o
n
g
o
v
er
n
m
en
tal
a
g
en
cies.
As
a
p
a
r
t
o
f
alter
n
ativ
e
en
er
g
y
y
ea
r
2
0
0
9
/2
0
1
0
g
o
v
er
n
m
en
t
o
f
Ne
p
al
p
lan
n
ed
to
in
s
tall
1
0
0
,
0
0
0
b
io
g
as
p
lan
ts
in
7
0
d
is
tr
icts
[
1
]
.
T
h
er
e
wer
e
2
0
1
7
7
5
b
io
g
as
p
lan
ts
in
2
0
0
9
,
in
s
talled
in
v
ar
io
u
s
d
is
tr
icts
o
f
Nep
al
[
1
,
p
g
6
0
]
.
Acc
o
r
d
in
g
to
2
0
1
1
ce
n
s
u
s
,
1
3
1
,
5
9
6
h
o
u
s
eh
o
ld
s
u
s
e
b
io
g
as f
o
r
co
o
k
in
g
,
wh
er
e
1
4
.
5
%
h
o
u
s
eh
o
ld
s
ar
e
i
n
u
r
b
an
ar
ea
s
an
d
8
5
.
5
%
a
r
e
in
r
u
r
al
ar
ea
s
.
T
h
is
is
2
.
5
2
%
o
f
th
e
to
tal
h
o
u
s
eh
o
ld
.
Ho
u
s
eh
o
ld
s
d
e
p
e
n
d
i
n
g
o
n
wo
o
d
/f
ir
ewo
o
d
f
o
r
c
o
o
k
i
n
g
a
r
e
6
3
.
9
9
%
.
T
h
e
u
s
e
o
f
Ker
o
s
en
e
f
o
r
co
o
k
in
g
is
in
1
.
0
2
%
o
f
th
e
h
o
u
s
eh
o
ld
s
,
wh
er
ea
s
L
PG
g
as
is
u
s
ed
in
2
1
.
0
3
%
f
o
r
th
e
s
am
e
p
u
r
p
o
s
e.
Ker
o
s
en
e
is
u
s
ed
in
1
8
.
2
8
%
h
o
u
s
eh
o
ld
s
f
o
r
co
o
k
in
g
p
u
r
p
o
s
es.
Similar
ly
h
o
u
s
eh
o
ld
s
u
s
in
g
elec
tr
icity
as
a
s
o
u
r
ce
o
f
lig
h
tin
g
is
6
7
.
2
%
[
2
]
.
E
v
id
en
ce
b
ased
s
tu
d
ies
ar
e
v
e
r
y
im
p
o
r
tan
t
f
o
r
m
an
y
co
u
n
tr
i
es
as
th
ey
lack
th
e
b
ac
k
b
o
n
e
o
f
s
tr
o
n
g
an
d
g
o
o
d
q
u
ality
o
f
f
icial
d
at
a.
Sp
ec
ial
s
tatis
tica
l
tech
n
iq
u
es
h
av
e
to
b
e
a
p
p
lied
a
n
d
d
ev
elo
p
ed
f
o
r
s
u
c
h
co
u
n
tr
ies with
lim
it
ed
an
d
s
ca
r
ce
d
ata.
Sev
er
al
f
ac
to
r
s
p
lay
i
n
g
a
cr
itical
r
o
le
in
en
er
g
y
co
n
s
u
m
p
tio
n
d
y
n
am
ics
n
ee
d
t
o
b
e
i
d
en
tifie
d
an
d
q
u
an
tifie
d
.
Her
e,
th
er
e
ar
e
s
ev
er
al
in
tan
g
ib
le
ad
v
an
tag
es
o
f
e
n
er
g
y
u
s
e
esp
ec
ially
in
r
u
r
al
ar
ea
s
.
T
h
ese
o
u
tweig
h
d
ir
ec
tly
attr
ib
u
tab
le
ad
v
an
tag
es
an
d
b
e
n
ef
its
.
I
n
th
is
p
ap
er
th
e
im
p
ac
t
o
f
v
ar
io
u
s
f
ac
to
r
s
g
o
v
er
n
i
n
g
th
e
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
d
y
n
am
ic
s
o
f
r
u
r
al
h
o
u
s
eh
o
ld
s
is
q
u
an
tifie
d
.
C
ateg
o
r
ical
d
ata
ar
e
an
aly
s
ed
an
d
p
o
ly
to
m
o
u
s
m
o
d
els
ar
e
d
ev
elo
p
ed
.
T
h
e
wo
r
k
is
n
o
v
el
as
u
n
lik
e
o
th
er
p
a
p
er
s
th
e
u
s
e
o
f
s
tatis
tics
i
s
n
o
t
s
u
p
er
f
icial.
Statis
tical
m
eth
o
d
s
ar
e
u
s
ed
in
g
en
er
ati
o
n
o
f
ca
teg
o
r
ical
d
ata
an
d
its
in
-
d
ep
th
a
n
aly
s
is
.
T
h
is
ap
p
r
o
ac
h
is
u
n
iq
u
e
t
o
en
er
g
y
r
e
s
ea
r
ch
p
r
o
b
lem
s
.
T
h
is
m
eth
o
d
is
u
s
ef
u
l
i
n
s
itu
atio
n
s
wh
er
e
we
h
av
e
lack
o
f
ac
cu
r
at
e
m
ea
s
u
r
em
en
t
in
s
tr
u
m
e
n
ts
.
I
t
ca
n
also
b
e
u
s
ef
u
l
in
s
itu
atio
n
s
wh
er
e
d
u
e
to
lac
k
o
f
awa
r
en
ess
,
ex
ac
t
a
n
s
wer
s
ca
n
n
o
t
b
e
f
u
r
n
is
h
ed
,
b
u
t
a
m
u
ltip
le
ch
o
ice
o
p
tio
n
ca
n
b
e
ch
o
s
en
co
r
r
ec
tly
.
Her
e
d
ep
e
n
d
en
t
v
ar
iab
les
a
r
e
class
if
ied
in
m
o
r
e
th
an
two
ca
teg
o
r
ies
an
d
a
r
e
h
en
ce
p
o
ly
to
m
o
u
s
an
d
n
o
t
d
ich
o
to
m
o
u
s
.
W
e
in
ter
p
r
et
th
e
p
a
r
am
et
er
s
o
f
th
ese
m
o
d
els.
W
e
also
ca
lcu
late
o
d
d
s
r
atio
an
d
o
d
d
s
in
f
av
o
u
r
f
o
r
q
u
an
ti
f
icatio
n
o
f
im
p
ac
t.
C
ateg
o
r
izi
n
g
d
ata
in
to
d
if
f
e
r
en
t
g
r
o
u
p
s
wh
ich
ca
n
b
e
later
r
ed
u
ce
d
to
o
r
d
i
n
al
d
ata
h
as
r
ed
u
ce
d
th
e
c
h
an
ce
s
o
f
er
r
o
r
d
u
e
to
am
b
i
g
u
o
u
s
r
esp
o
n
s
e.
T
h
u
s
th
e
d
y
n
am
ics
o
f
ch
an
g
e
o
f
v
ar
ia
b
les
r
elate
d
to
en
er
g
y
co
n
s
u
m
p
tio
n
o
f
7
0
0
h
o
u
s
eh
o
l
d
s
s
u
ch
as
tim
e
s
p
en
t
in
th
e
co
llectio
n
o
f
f
ir
ewo
o
d
,
ty
p
e
o
f
h
o
u
s
e,
a
m
o
u
n
t
o
f
f
ir
ewo
o
d
s
av
ed
,
tim
e
s
av
ed
,
em
p
lo
y
er
an
d
s
ch
o
o
l
l
o
ca
ted
with
in
1
5
m
in
d
is
tan
ce
ar
e
m
in
u
tely
a
n
aly
ze
d
.
T
h
e
d
ata
u
s
ed
h
e
r
e
is
b
ased
o
n
s
am
p
le
s
u
r
v
ey
o
f
3
0
0
h
o
u
s
eh
o
ld
s
o
f
n
o
r
m
al
u
s
er
s
wh
ich
ar
e
n
ati
o
n
al
g
r
id
en
er
g
y
u
s
er
s
an
d
4
0
0
h
o
u
s
eh
o
ld
s
o
f
b
io
g
as u
s
er
s
.
Dev
k
o
ta
[
3
]
h
as
d
is
cu
s
s
ed
an
d
d
ev
elo
p
ed
s
ev
er
al
s
tatis
ti
ca
l
m
eth
o
d
s
f
o
r
co
u
n
tr
ies
with
lim
ited
an
d
s
ca
r
ce
d
ata
d
em
o
g
r
ap
h
ic
d
ata.
T
h
is
a
p
p
r
o
ac
h
h
as
wid
e
ap
p
licab
ilit
y
in
v
ar
io
u
s
in
te
r
d
is
cip
lin
ar
y
f
ield
s
.
Saleh
et
al.
[
4
]
im
p
lem
e
n
ted
s
tatis
tical
o
p
tim
izatio
n
o
f
p
a
r
a
m
eter
s
an
d
co
n
d
itio
n
s
f
o
r
r
ed
u
ctio
n
o
f
co
n
s
u
m
ed
ch
em
icals
an
d
r
ea
g
en
ts
i
n
ex
p
er
im
en
tal
wo
r
k
s
.
Kan
a
k
e
t
a
l.
[
5
]
ap
p
lied
s
tatis
tics
an
d
ar
tific
ial
in
tellig
en
ce
an
d
d
ev
elo
p
e
d
m
o
d
els
with
ai
m
to
r
ed
u
c
e
th
e
r
is
k
o
f
in
cid
e
n
ce
o
f
b
l
o
o
d
r
elate
d
an
em
ia.
Statis
t
ic
al
an
aly
s
is
w
a
s
u
s
e
d
i
n
e
v
a
l
u
at
i
o
n
o
f
c
o
r
r
o
s
i
o
n
r
es
i
s
t
a
n
t
s
te
e
l
b
a
r
s
i
n
s
u
s
t
a
i
n
a
b
l
e
b
u
i
l
d
i
n
g
c
o
n
s
t
r
u
ct
i
o
n
b
y
I
m
a
m
et
a
l
.
[
6
]
.
R
u
i
et
al.
[
7
]
h
a
v
e
an
aly
s
ed
c
au
s
es,
ch
ar
a
cter
is
tics
an
d
co
n
s
eq
u
en
ce
s
o
f
tu
n
n
el
f
ir
e
ac
cid
en
ts
in
C
h
in
a
u
s
in
g
s
tatis
t
ical
m
eth
o
d
s
.
Pach
ec
o
et
al.
[
8
]
an
aly
ze
d
th
e
co
m
p
r
e
s
s
iv
e
s
tr
en
g
th
o
f
th
r
ee
P
o
r
tu
g
u
ese
ce
m
en
t
b
r
an
d
u
s
in
g
p
r
o
b
ab
ilis
tic
m
o
d
ellin
g
.
B
h
attac
h
ar
y
y
a
[
9
]
s
tu
d
ied
th
e
ac
ce
s
s
o
f
e
n
er
g
y
to
I
n
d
ia’
s
p
o
o
r
.
W
h
er
ea
s
an
o
v
er
v
iew
o
f
e
n
er
g
y
co
n
s
u
m
p
tio
n
p
atter
n
b
y
av
ailab
le
d
ata
an
d
t
h
e
an
al
y
s
is
o
f
s
o
m
e
r
elev
an
t
asp
ec
ts
o
f
en
er
g
y
p
o
licy
in
r
u
r
al
C
h
in
a
ar
e
p
r
esen
ted
b
y
Z
h
an
g
et
al.
[
1
0
]
.
Petr
id
es
an
d
Fu
r
n
h
am
[
1
1
]
an
aly
ze
d
s
ev
er
a
l
d
im
en
s
io
n
s
h
u
m
an
’
s
e
m
o
tio
n
a
l
in
tellig
en
ce
with
e
x
p
lo
r
ato
r
y
f
ac
to
r
an
aly
s
is
.
Similar
ly
Gar
cia
et
al
.
[
1
2
]
u
s
e
d
m
u
ltiv
ar
iate
s
tatis
tics
is
u
s
ed
to
esti
m
ate
th
e
th
eo
r
etica
l,
tec
h
n
ical
an
d
ec
o
n
o
m
ic
p
o
ten
tials
o
f
b
io
m
ass
es
f
o
r
b
io
en
er
g
y
p
r
o
d
u
ctio
n
.
T
h
ey
u
s
ed
Hier
ar
ch
ial
clu
s
ter
ag
g
lo
m
er
ates
an
d
Prin
cip
le
co
m
p
o
n
en
ts
.
Su
n
et
al.
[
1
3
]
u
s
ed
s
to
ch
asti
c
p
r
o
ce
s
s
es to
s
e
lect
f
r
o
m
lar
g
e
n
u
m
b
er
o
f
s
ce
n
ar
io
s
f
o
r
r
ep
r
esen
tin
g
t
h
e
v
ar
iab
ilit
y
o
f
o
p
e
r
a
t
i
n
g
p
o
i
n
t
s
,
a
s
u
i
t
a
b
l
e
s
c
e
n
a
r
i
o
.
T
h
i
s
w
as
u
s
e
d
i
n
t
r
a
n
s
m
is
s
i
o
n
n
e
t
w
o
r
k
e
x
p
a
n
s
i
o
n
p
l
a
n
n
i
n
g
.
Xu
e
t
a
l
.
[
1
4
]
u
s
ed
m
u
ltiv
ar
iate
s
tatis
tica
l
r
eg
r
ess
io
n
m
o
d
el
to
ac
cu
r
ately
p
r
e
d
i
c
t
t
h
e
o
u
t
p
u
t
p
o
w
e
r
o
f
p
h
o
t
o
v
o
l
t
a
i
c
g
r
i
d
-
c
o
n
n
e
c
t
e
d
p
o
w
e
r
g
e
n
e
r
a
t
i
o
n
.
T
h
i
s
s
ec
tio
n
is
f
o
llo
wed
b
y
s
ec
tio
n
2
o
n
th
eo
r
etica
l
b
ac
k
g
r
o
u
n
d
a
n
d
h
y
p
o
th
esis
titl
ed
r
esear
ch
m
eth
o
d
s
wh
ich
is
f
o
l
lo
wed
b
y
s
ec
tio
n
3
o
n
r
esu
lts
an
d
d
is
cu
s
s
io
n
.
T
h
is
is
f
o
llo
wed
b
y
a
s
ec
tio
n
4
titl
ed
c
o
n
clu
s
io
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
C
ateg
o
r
ical
d
ata
is
th
e
o
n
ly
m
ea
n
s
o
f
g
ettin
g
ac
cu
r
ate
d
ata
i
n
v
ar
io
u
s
s
tu
d
ies.
C
ateg
o
r
izin
g
d
ata
in
to
s
ev
er
al
g
r
o
u
p
s
r
ed
u
ce
s
th
e
ch
an
ce
s
o
f
h
a
v
in
g
a
m
b
ig
u
o
u
s
an
d
er
r
o
n
eo
u
s
d
ata.
T
h
is
is
esp
ec
ially
tr
u
e
f
o
r
co
u
n
tr
ies
with
lim
ited
d
ata.
Du
e
to
lack
o
f
awa
r
en
ess
am
o
n
g
v
a
r
io
u
s
s
tak
eh
o
ld
er
s
an
d
a
v
er
y
lim
ited
(
o
f
ten
u
n
r
eliab
le)
d
atab
ase,
ca
teg
o
r
ical
d
ata
m
in
im
ize
th
e
am
b
ig
u
ity
o
f
r
esp
o
n
s
e
b
etwe
en
in
ter
v
i
ewe
e
an
d
in
ter
v
iewe
r
.
T
h
ese
d
ata
ar
e
a
ls
o
u
s
ef
u
l
wh
en
t
h
e
m
ea
s
u
r
e
m
en
t
in
s
tr
u
m
en
ts
o
f
d
ata
c
o
llectio
n
ar
e
n
o
t
v
e
r
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
R
o
le
o
f lo
ca
tio
n
o
f h
o
u
s
eh
o
l
d
a
n
d
its
s
o
cio
-
ec
o
n
o
mic
s
ta
tu
s
o
n
en
erg
y
c
o
n
s
u
mp
tio
n
…
(
Jy
o
ti U.
Dev
ko
ta
)
195
p
r
ec
is
e
an
d
ac
cu
r
ate
.
I
d
e
n
tific
atio
n
o
f
all
th
e
p
o
s
s
ib
le
r
esp
o
n
s
e
to
a
q
u
esti
o
n
an
d
t
h
en
p
u
ttin
g
th
em
i
n
to
s
ev
er
al
ca
teg
o
r
ies
r
ed
u
ce
s
th
e
ch
an
ce
s
o
f
g
en
er
atin
g
f
au
lty
d
ata
d
u
e
to
u
n
ce
r
tain
ty
a
n
d
v
ag
u
en
ess
.
T
h
ese
d
ata
ca
n
b
e
class
if
ied
in
s
u
ch
a
w
ay
th
at
th
e
r
esu
ltin
g
d
ata
ca
n
b
e
r
e
d
u
ce
d
to
p
u
t
o
n
o
r
d
in
al
s
ca
le.
Du
e
to
lar
g
e
s
am
p
le
s
ize
th
e
o
r
d
in
al
d
ata
ca
n
b
e
tr
ea
ted
as
co
n
tin
u
o
u
s
d
ata
b
y
ce
n
tr
a
l
lim
it
th
eo
r
em
.
As
th
e
d
ep
en
d
e
n
t
v
ar
iab
le
tak
es m
o
r
e
th
an
two
v
alu
es,
p
o
ly
to
m
o
u
s
m
o
d
els in
s
tead
d
ich
o
to
m
o
u
s
m
o
d
el
s
ar
e
s
u
itab
le.
2
.
1
.
M
ea
s
ure
s
T
h
e
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
s
o
f
attr
ib
u
tes
I
an
d
J
ca
n
b
e
cr
o
s
s
tab
u
lated
in
to
co
n
tin
g
en
c
y
tab
les
in
th
e
f
o
llo
win
g
m
a
n
n
er
as
s
h
o
w
n
in
T
ab
le
1
.
T
h
is
cr
o
s
s
tab
u
latio
n
g
iv
es
a
b
etter
o
v
er
v
iew
o
f
th
e
d
ata.
Sp
ec
ial
ca
s
e
o
f
th
is
I
×
J
co
n
tin
g
e
n
cy
ta
b
le
is
a
2
×
2
co
n
tin
g
en
c
y
tab
le,
wh
ich
is
g
iv
en
in
T
ab
le
2
.
T
ab
le
1
.
C
o
n
tin
g
en
cy
ta
b
le
o
f
o
r
d
er
I
×
J
T
ab
le
2
.
C
o
n
tin
g
en
cy
ta
b
le
o
f
o
r
d
er
2
×
2
j
i
1
2
3
……
….
J
To
t
a
l
1
11
12
13
……
……
1
1
.
2
21
22
23
……
……
2
2
.
….
….
….
….
.
I
1
2
13
……
……
.
To
t
a
l
.
1
.
2
.
3
…….
.
………
.
i
i
1
2
To
t
a
l
1
11
12
1
.
2
21
22
2
.
To
t
a
l
.
1
.
2
Od
d
s
r
atio
ca
n
b
e
u
s
ed
i
n
q
u
a
n
tify
in
g
th
e
im
p
ac
t
o
f
a
tech
n
i
q
u
e.
T
h
is
is
d
o
n
e
b
y
ca
lcu
latin
g
th
e
r
atio
o
f
two
c
o
n
d
itio
n
al
p
r
o
b
ab
ilit
i
es
o
f
d
if
f
er
en
t
r
esp
o
n
s
e
v
alu
e
s
u
n
d
er
th
e
s
am
e
c
o
n
d
itio
n
.
W
h
en
th
e
r
esp
o
n
s
e
is
d
ich
o
to
m
o
u
s
th
en
th
e
o
d
d
s
ar
e
[
1
5
]
:
1
|
2
|
=
1
|
1
−
1
|
=
1
2
s
o
in
th
e
2
×
2
tab
le
g
iv
e
n
ab
o
v
e
f
o
r
j=
1,
1
|
1
2
|
1
=
1
|
1
1
−
1
|
1
=
11
21
f
o
r
j
=2
,
1
|
2
2
|
2
=
1
|
2
1
−
1
|
2
=
12
22
a
ls
o
1
|
=
1
.
, j
=1
,
2
.
B
ased
o
n
p
r
o
d
u
ct
law
o
f
c
o
m
p
o
u
n
d
e
v
en
ts
;
1
|
1
=
µ
′
1
′
2
|
1
=
µ
′
(
1
−
1
′
)
ln
(
1
|
1
2
|
1
)
=
ln
(
1
|
1
1
−
1
|
1
)
=
ln
(
µ
′
1
′
1
−
µ
′
1
′
)
=
µ
+
1
ln
(
1
|
2
|
)
=
ln
(
1
|
1
−
1
|
)
=
µ
+
=
1
,
2
o
r
,
ln
(
1
|
√
2
|
1
|
)
=
µ
+
…
(
1
)
o
r,
l
og
(
1
|
̇
)
=
+
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
7
9
2
I
n
t J
Ap
p
l Po
wer
E
n
g
,
Vo
l.
9
,
No
.
3
,
Dec
em
b
e
r
2
0
2
0
:
1
9
3
–
204
196
̇
=
(
∏
|
)
1
2
,
=
1
,
2
as,
ln
(
√
1
|
2
|
)
=
µ
+
o
r
,
1
2
ln
(
1
|
2
|
)
=
µ
+
ln
(
1
|
1
−
1
|
)
=
µ
+
1
|
1
−
1
|
=
1
|
=
+
1
+
+
2
|
=
1
−
1
|
=
1
1
+
+
W
h
en
th
e
r
esp
o
n
s
e
v
ar
ia
b
le
is
n
o
t
d
ich
o
to
m
o
u
s
th
at
is
it
h
as
m
o
r
e
th
an
two
o
p
tio
n
s
,
th
e
n
i
t
is
ca
lled
p
o
ly
to
m
o
u
s
.
S
o
p
o
ly
to
m
o
u
s
r
esp
o
n
s
e
m
o
d
els
h
a
v
e
m
o
r
e
th
a
n
two
ca
teg
o
r
ies,
s
ay
m
o
r
e
th
a
n
two
ca
teg
o
r
ies
in
r
o
w
o
r
c
o
lu
m
n
o
r
b
o
th
.
T
h
is
ca
n
b
e
ex
p
lain
ed
b
y
m
u
ltin
o
m
i
al
p
r
o
b
a
b
i
lity
d
en
s
ity
f
u
n
ctio
n
.
Her
e
:
l
og
(
̇
)
=
1
∑
l
og
(
|
)
w
h
er
e
i =
1
,
2
,
…I
; j
=
1
,
2
,
…J
.
π
̇
j
i
s
th
e
v
alu
e
o
f
th
e
p
r
o
b
ab
il
ity
o
b
tain
e
d
in
th
is
way
f
o
r
th
e
j
th
ca
teg
o
r
y
o
f
th
e
ex
p
lan
ato
r
y
v
ar
iab
le
s
.
T
h
is
v
alu
e
is
ca
lled
th
e
g
eo
m
etr
ic
m
e
an
.
B
ac
k
-
tr
a
n
s
f
o
r
m
atio
n
,
elim
in
atin
g
th
e
l
o
g
ar
ith
m
,
s
h
o
ws
its
d
ef
in
itio
n
:
̇
=
(
∏
|
)
1
So
,
p
o
ly
to
m
o
u
s
lo
g
is
tic
m
o
d
els with
o
n
e
ex
p
lan
at
o
r
y
v
ar
iab
l
e
ca
n
b
e
wr
itten
as
:
l
og
(
|
̇
)
=
+
i =
1
,
2
,
…I
; j
=
1
,
2
,
…J
(
1
)
H
er
e
th
er
e
is
o
n
e
s
u
ch
eq
u
at
io
n
f
o
r
ea
c
h
ca
teg
o
r
y
j
o
f
ex
p
lan
ato
r
y
v
ar
iab
le,
as
well
as
f
o
r
ea
ch
ca
teg
o
r
y
o
f
r
esp
o
n
s
e
i.
T
h
is
is
a
s
et
o
f
×
lin
ea
r
eq
u
atio
n
s
to
d
escr
ib
e
h
o
w
th
e
m
u
ltin
o
m
ial
i
s
ch
an
g
in
g
in
d
if
f
er
en
t
ca
teg
o
r
ies
o
f
th
e
ex
p
lan
ato
r
y
v
ar
iab
le.
B
ec
au
s
e
th
e
r
esp
o
n
s
e
ca
teg
o
r
ies
ar
e
co
m
p
ar
ed
with
a
m
ea
n
f
o
llo
win
g
c
o
n
s
tr
ain
ts
ar
e
i
m
p
o
s
ed
o
n
th
e
p
ar
am
eter
s
:
∑
=
0
,
∑
=
0
∀
an
d
∑
=
0
∀
.
T
h
e
esti
m
ates
m
ay
b
e
o
b
tain
ed
b
y
s
o
lv
i
n
g
I
s
ets
o
f
J
e
q
u
atio
n
s
.
Po
ly
to
m
o
u
s
m
o
d
el
g
iv
en
in
(
1
)
s
at
is
f
y
p
r
o
p
er
ties
th
at
ar
e
g
iv
en
b
elo
w
b
y
(
2
)
an
d
(
3
)
:
ln
(
|
̇
)
=
+
̇
=
(
∏
|
)
1
=
(
1
|
2
|
…
…
…
|
)
1
,
=
1
,
…
a
ls
o
,
(
1
|
̇
)
(
2
|
̇
)
(
3
|
̇
)
…
.
.
(
|
̇
)
=
1
(
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
R
o
le
o
f lo
ca
tio
n
o
f h
o
u
s
eh
o
l
d
a
n
d
its
s
o
cio
-
ec
o
n
o
mic
s
ta
tu
s
o
n
en
erg
y
c
o
n
s
u
mp
tio
n
…
(
Jy
o
ti U.
Dev
ko
ta
)
197
o
r
,
ln
(
1
|
̇
)
+
(
2
|
̇
)
+
(
3
|
̇
)
+
⋯
.
.
+
(
|
̇
)
=
0
L
HS o
f
(
2
)
;
1
|
2
|
3
|
…
…
…
…
…
|
̇
=
1
|
2
|
3
|
…
…
…
…
…
|
(
1
|
2
|
3
|
…
…
…
…
…
|
)
=
1
a
ls
o
,
ln
(
|
̇
)
−
ln
(
|
̇
)
=
ln
(
|
|
)
(
3)
as,
L
H
S
;
ln
(
|
̇
)
−
ln
(
|
̇
)
=
ln
(
|
̇
|
̇
)
=
ln
(
|
|
)
=
.
.
Hy
p
o
th
esis
,
t
h
er
e
is
d
e
p
en
d
e
n
ce
b
etwe
en
th
e
lo
ca
tio
n
an
d
s
o
cio
-
ec
o
n
o
m
ic
s
tatu
s
o
f
th
e
h
o
u
s
e
o
n
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
d
y
n
am
ics
.
Hy
p
o
th
esis
1
:
Fo
r
h
o
u
s
eh
o
ld
li
v
in
g
with
in
1
5
m
in
u
tes
to
th
e
s
ch
o
o
l
th
e
d
ep
en
d
e
n
ce
o
n
f
ir
ewo
o
d
is
less
(
n
o
r
m
al
u
s
er
s
)
Hy
p
o
th
esis
2
:
Fo
r
h
o
u
s
eh
o
ld
liv
in
g
with
in
1
5
m
in
u
tes
to
t
h
e
em
p
lo
y
er
th
e
d
ep
e
n
d
en
ce
o
n
f
ir
ewo
o
d
is
l
ess
(
n
o
r
m
al
u
s
er
s
)
Hy
p
o
th
esis
3
:
L
o
w
s
o
cio
ec
o
n
o
m
ic
s
tatu
s
(
in
d
icate
d
b
y
th
e
ty
p
e
o
f
h
o
u
s
e)
im
p
lies
m
o
r
e
tim
e
s
p
en
t
o
n
th
e
co
llectio
n
o
f
f
ir
ewo
o
d
(
n
o
r
m
al
u
s
er
s
)
Hy
p
o
th
esis
4
:
L
o
w
s
o
cio
ec
o
n
o
m
ic
s
tatu
s
(
in
d
icate
d
b
y
t
h
e
ty
p
e
o
f
h
o
u
s
e)
im
p
lies
m
o
r
e
k
ilo
g
r
a
m
s
o
f
f
ir
ewo
o
d
c
o
n
s
u
m
ed
(
n
o
r
m
al
u
s
er
s
)
Hy
p
o
th
esis
5
:
L
o
w
s
o
cio
ec
o
n
o
m
ic
s
tatu
s
(
in
d
icate
d
b
y
th
e
ty
p
e
o
f
h
o
u
s
e)
im
p
lies
less
litt
er
s
o
f
k
er
o
s
en
e
co
n
s
u
m
ed
(
n
o
r
m
al
u
s
er
s
)
Hy
p
o
th
esis
6
:
L
o
w
s
o
cio
ec
o
n
o
m
ic
s
tatu
s
o
f
b
i
o
g
as
o
w
n
er
s
(
in
d
icate
d
b
y
th
e
ty
p
e
o
f
h
o
u
s
e)
im
p
lies
m
o
r
e
tim
e
s
p
en
t o
n
th
e
c
o
llectio
n
o
f
f
ir
ewo
o
d
b
ef
o
r
e
th
e
in
s
tallatio
n
o
f
p
lan
t
Hy
p
o
th
esis
7
:
L
o
w
s
o
cio
ec
o
n
o
m
ic
s
tatu
s
o
f
b
i
o
g
as
o
w
n
er
s
(
in
d
icate
d
b
y
th
e
ty
p
e
o
f
h
o
u
s
e)
im
p
lies
m
o
r
e
tim
e
s
p
en
t o
n
th
e
c
o
llectio
n
o
f
f
ir
ewo
o
d
a
f
ter
th
e
i
n
s
tallatio
n
o
f
p
lan
t
Hy
p
o
th
esis
8
:
L
o
w
s
o
cio
ec
o
n
o
m
ic
s
tatu
s
(
in
d
icate
d
b
y
ty
p
e
o
f
h
o
u
s
e)
im
p
lies
m
o
r
e
tim
e
s
av
ed
af
ter
co
n
s
tr
u
ctio
n
o
f
b
io
g
as p
lan
t
Hy
p
o
th
esis
9
:
L
o
w
s
o
cio
ec
o
n
o
m
ic
s
tatu
s
(
i
n
d
icate
d
b
y
ty
p
e
o
f
h
o
u
s
e)
im
p
lies
m
o
r
e
f
i
r
ewo
o
d
s
av
ed
a
f
t
er
a
s
witch
o
v
er
to
b
i
o
g
as p
lan
t f
o
r
co
o
k
in
g
Hy
p
o
th
esis
1
0
:
Mo
r
e
tim
e
s
p
en
t
o
n
co
llectio
n
o
f
f
ir
ewo
o
d
b
ef
o
r
e
th
e
co
n
s
tr
u
ct
io
n
o
f
b
io
g
as
p
lan
t
im
p
lies
“r
elativ
ely
”
m
o
r
e
tim
e
s
p
e
n
t i
n
af
ter
th
e
c
o
n
s
tr
u
ctio
n
o
f
b
io
g
as p
lan
t
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Da
t
a
T
h
e
p
r
im
ar
y
d
ata
co
llected
f
o
r
th
is
s
tu
d
y
ar
e
f
r
o
m
7
0
0
h
o
u
s
eh
o
ld
s
.
T
h
ey
ar
e
o
b
tain
ed
f
r
o
m
two
s
am
p
le
s
u
r
v
ey
s
o
f
3
0
0
a
n
d
4
0
0
h
o
u
s
eh
o
ld
s
o
f
n
o
r
m
al
e
n
er
g
y
u
s
er
s
a
n
d
b
io
g
as
c
o
n
s
u
m
er
s
r
esp
ec
tiv
ely
.
T
h
ese
ar
e
h
o
u
s
eh
o
l
d
s
in
h
ab
itin
g
in
d
if
f
er
en
t
r
e
g
io
n
s
o
f
Nep
al.
I
n
th
ese
two
s
u
r
v
ey
s
th
e
b
asic
s
et
o
f
q
u
esti
o
n
s
was
th
e
s
am
e,
o
n
ly
s
o
m
e
q
u
esti
o
n
s
to
b
e
ask
ed
f
r
o
m
th
ese
two
d
if
f
er
en
t
ca
te
g
o
r
ies
o
f
r
esp
o
n
d
e
n
ts
wer
e
m
o
d
i
f
ied
.
Pre
-
test
o
f
q
u
esti
o
n
n
air
e
an
d
tr
ain
in
g
to
th
e
in
ter
v
iewe
r
en
s
u
r
ed
th
e
q
u
ality
o
f
co
llected
d
ata.
T
h
e
p
o
s
s
ib
le
r
esp
o
n
s
e
was
p
r
o
v
id
ed
as
a
m
u
ltip
le
ch
o
ice
o
p
tio
n
with
an
s
wer
s
clas
s
if
ied
o
n
an
o
r
d
in
al
s
ca
le.
T
h
e
d
etails
o
f
v
ar
iab
les
ar
e
g
iv
e
n
in
T
a
b
le
3
.
T
h
is
T
ab
le
3
s
h
o
ws
th
at
v
ar
ia
b
les
an
aly
ze
d
h
er
e
ar
e
ca
te
g
o
r
ical
d
ata
class
if
ied
o
n
o
r
d
i
n
al
s
ca
le.
As
th
e
s
am
p
le
s
ize
is
lar
g
e
th
is
o
r
d
in
al
d
ata
ca
n
b
e
tr
ea
ted
as
a
co
n
tin
u
o
u
s
d
ata
b
y
u
s
in
g
th
e
ce
n
tr
al
lim
it th
eo
r
em
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
7
9
2
I
n
t J
Ap
p
l Po
wer
E
n
g
,
Vo
l.
9
,
No
.
3
,
Dec
em
b
e
r
2
0
2
0
:
1
9
3
–
204
198
3
.
2
.
Resul
t
T
h
e
r
esp
o
n
s
es
to
q
u
esti
o
n
s
in
th
e
q
u
esti
o
n
n
air
e
wer
e
s
tr
u
ctu
r
ed
.
All
th
e
p
o
s
s
ib
le
an
s
wer
s
to
ea
ch
q
u
esti
o
n
wer
e
p
r
o
p
er
ly
wo
r
k
ed
o
u
t
d
u
r
in
g
th
e
p
r
e
-
test
.
I
t
was
m
en
tio
n
ed
as
a
m
u
ltip
le
ch
o
ice
o
p
tio
n
.
T
h
is
r
esu
lted
in
a
ca
teg
o
r
ical
d
ata
th
at
co
u
ld
b
e
class
if
ied
o
n
o
r
d
in
al
s
ca
le.
Fo
r
ex
am
p
le
as
we
s
ee
f
r
o
m
T
ab
le
3
,
th
e
‘
tim
e
s
p
en
t
in
th
e
c
o
llectio
n
o
f
f
ir
ewo
o
d
’
f
o
r
b
i
o
g
as
u
s
er
s
is
class
if
ied
in
to
s
ix
ca
teg
o
r
ies.
Acc
o
r
d
in
g
to
th
e
am
o
u
n
t
o
f
tim
e
d
ev
o
te
d
f
o
r
th
e
co
llectio
n
o
f
f
ir
ewo
o
d
,
th
ese
ca
teg
o
r
ies
a
r
e
lab
elled
as
0
,
1
,
2
,
3
,
4
,
5
.
Her
e
0
s
tan
d
s
f
o
r
n
o
tim
e
s
p
e
n
t
wh
er
ea
s
5
d
en
o
te
m
ax
im
u
m
tim
e
o
f
1
-
2
h
o
u
r
s
s
p
en
t.
T
h
ese
n
u
m
b
er
s
ar
e
o
n
o
r
d
in
al
s
ca
le
as
th
ese
v
alu
es
s
ig
n
if
y
th
e
am
o
u
n
t
o
f
tim
e
s
p
en
t.
As
s
ee
n
f
r
o
m
T
a
b
le
3
,
th
is
h
o
ld
s
tr
u
e
f
o
r
n
atio
n
al
g
r
i
d
en
e
r
g
y
u
s
er
s
.
Sam
e
is
tr
u
e
f
o
r
th
e
attr
ib
u
te
‘
ty
p
e
o
f
h
o
u
s
e
’
lab
elled
as
1
,
2
,
3
a
n
d
4
f
o
r
b
o
th
b
io
g
as
u
s
er
s
a
n
d
g
r
i
d
e
n
er
g
y
u
s
er
s
.
Her
e
h
o
u
s
eh
o
ld
wi
th
co
n
cr
ete
h
o
u
s
e
with
lab
e
l
1
is
h
ig
h
est
in
th
e
s
o
cio
ec
o
n
o
m
ic
s
tatu
s
wh
e
r
ea
s
th
e
m
u
d
h
o
u
s
e
lab
e
l
led
4
r
an
k
s
lo
wes
t
in
th
e
s
o
ci
o
ec
o
n
o
m
ic
s
tatu
s
.
Fo
r
th
e
attr
ib
u
tes
‘
am
o
u
n
t o
f
f
ir
ew
o
o
d
s
av
ed
an
d
am
o
u
n
t
o
f
tim
e
s
av
ed
’
,
th
e
v
alu
es lab
els ar
e
0
,
1
,
2
a
n
d
3
,
1
,
2
,
3
an
d
4
r
esp
ec
tiv
ely
.
T
h
ese
n
u
m
b
er
s
ar
e
also
class
if
ied
o
n
o
r
d
in
al
s
ca
le
g
o
in
g
f
r
o
m
s
m
allest
to
h
ig
h
est
as
th
e
am
o
u
n
t
o
f
f
ir
ewo
o
d
s
av
ed
an
d
am
o
u
n
t
o
f
tim
e
s
av
e
d
in
cr
ea
s
es.
Similar
ly
,
‘
d
is
tan
ce
f
r
o
m
em
p
lo
y
er
’
a
n
d
‘
d
i
s
t
a
n
c
e
f
r
o
m
s
c
h
o
o
l
’
a
r
e
a
ls
o
c
l
a
s
s
i
f
ie
d
o
n
o
r
d
i
n
a
l
s
c
al
e
o
f
1
,
2
p
r
o
p
o
r
t
i
o
n
a
l
t
o
t
h
e
c
l
o
s
e
n
ess
f
r
o
m
t
h
e
s
e
p
l
a
c
es
.
T
ab
le
3
.
Deta
ils
o
f
ca
teg
o
r
ical
d
ata
U
ser
/
S
a
m
p
l
e
si
z
e
V
a
r
i
a
b
l
e
n
a
me
V
a
l
u
e
s
o
n
o
r
d
i
n
a
l
s
c
a
l
e
F
r
e
q
u
e
n
c
y
B
i
o
g
a
s
(
4
0
0
h
o
u
s
e
h
o
l
d
)
Ti
me
sp
e
n
t
i
n
t
h
e
c
o
l
l
e
c
t
i
o
n
o
f
f
i
r
e
w
o
o
d
b
e
f
o
r
e
N
o
t
i
me
sp
e
n
t
0
33
<
1
5
m
i
n
u
t
e
s
1
15
15
-
3
0
mi
n
u
t
e
s
2
21
30
-
4
5
mi
n
u
t
e
s
3
18
45
-
6
0
mi
n
u
t
e
s
4
53
1
h
o
u
r
-
2
h
o
u
r
s
5
2
6
0
Ti
me
sp
e
n
t
i
n
t
h
e
c
o
l
l
e
c
t
i
o
n
o
f
f
i
r
e
w
o
o
d
a
f
t
e
r
N
o
t
i
me
sp
e
n
t
0
1
5
3
<
1
5
m
i
n
u
t
e
s
1
1
3
7
15
-
3
0
mi
n
u
t
e
s
2
67
30
-
4
5
mi
n
u
t
e
s
3
24
45
-
6
0
mi
n
u
t
e
s
4
7
1
h
o
u
r
-
2
h
o
u
r
s
5
12
Ty
p
e
o
f
h
o
u
s
e
C
o
n
c
r
e
t
e
1
1
6
6
Ti
l
e
/
a
sb
e
st
o
s
2
17
M
o
d
e
r
n
l
i
g
h
t
r
o
o
f
3
77
M
u
d
h
o
u
s
e
4
1
4
0
A
mo
u
n
t
o
f
f
i
r
e
w
o
o
d
s
a
v
e
d
a
f
t
e
r
b
i
o
g
a
s
U
p
t
o
3
0
K
g
1
39
30
-
5
0
K
g
2
1
1
5
A
b
o
v
e
5
0
K
g
3
2
4
6
Ti
me
sa
v
e
d
N
o
c
h
a
n
g
e
0
13
Le
ss
t
h
a
n
6
0
m
i
n
u
t
e
s
1
1
3
2
1
h
o
u
r
t
o
3
h
o
u
r
s
2
1
9
5
3
-
5
h
o
u
r
s
3
23
M
o
r
e
t
h
a
n
5
h
o
u
r
s
4
37
N
o
r
mal
(
3
0
0
h
o
u
s
e
h
o
l
d
)
Ti
me
f
o
r
t
h
e
c
o
l
l
e
c
t
i
o
n
o
f
f
i
r
e
w
o
o
d
N
o
t
a
p
p
l
i
c
a
b
l
e
0
13
<
1
5
m
i
n
u
t
e
s
1
29
15
-
3
0
mi
n
u
t
e
s
2
1
7
6
30
-
4
5
mi
n
u
t
e
s
3
78
4
5
mi
n
-
1
h
o
u
r
4
04
Ty
p
e
o
f
h
o
u
s
e
C
o
n
c
r
e
t
e
h
o
u
se
1
4
Ti
l
e
d
/
a
s
b
e
s
t
o
s
2
89
M
o
d
e
r
n
l
i
g
h
t
r
o
o
f
3
1
8
5
M
u
d
h
o
u
s
e
4
22
Emp
l
o
y
e
r
w
i
t
h
i
n
1
5
m
i
n
u
t
e
s
Y
e
s
1
17
No
2
2
8
3
S
c
h
o
o
l
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1
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a
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in
T
ab
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ab
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5
.
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s
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tab
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s
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g
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d
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w
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To
t
a
l
0
1
2
3
4
I
s t
h
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r
s
c
h
o
o
l
w
i
t
h
i
n
1
5
m
i
n
u
t
e
s
No
12
26
1
1
0
64
2
2
1
4
W
a
l
k
i
n
g
d
i
st
a
n
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s
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t
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13
29
1
7
6
78
4
3
0
0
No
w
th
e
d
ata
in
T
a
b
le
5
is
m
o
d
elled
u
s
in
g
th
e
f
o
llo
win
g
m
et
h
o
d
o
lo
g
y
:
ln
(
|
̇
)
=
+
l
og
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̂
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)
−
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l
og
(
̂
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)
=
l
og
(
)
−
1
∑
l
og
(
)
=
+
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
7
9
2
I
n
t J
Ap
p
l Po
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E
n
g
,
Vo
l.
9
,
No
.
3
,
Dec
em
b
e
r
2
0
2
0
:
1
9
3
–
204
200
Fro
m
T
ab
le
5
we
g
et
th
e
f
o
llo
win
g
r
esu
lts
;
ln
(
12
)
+
ln
(
26
)
+
ln
(
110
)
+
ln
(
64
)
+
ln
(
2
)
5
=
3
.
059103
ln
(
1
)
+
ln
(
3
)
+
ln
(
66
)
+
ln
(
14
)
+
ln
(
2
)
5
=
1
.
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2
4
0
9
4
ln
(
12
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3
.
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1
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21
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7
4
2
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1
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21
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1
.
7
2
4
0
9
21
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1
=
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1
.
1492
,
11
=
−
0
.
57495
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21
=
0
.
57495
ln
(
26
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−
3
.
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12
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(
3
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1
.
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22
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1
9
8
9
9
4
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22
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6
2
5
4
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22
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2
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4133
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ly
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2
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14
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.
6985
,
15
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,
25
=
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6675
T
h
e
v
alu
es
o
f
th
e
p
ar
am
eter
s
o
b
tain
ed
f
r
o
m
cr
o
s
s
tab
u
lati
o
n
d
ata
p
r
o
v
id
ed
T
a
b
le
5
is
wr
itten
in
T
ab
le
6
.
Fro
m
T
ab
le
6
we
g
et
th
at,
th
e
av
er
ag
e
o
d
d
s
in
f
av
o
u
r
o
f
n
o
tim
e
s
p
en
t
in
th
e
co
llectio
n
o
f
f
ir
ewo
o
d
v
er
s
u
s
4
5
m
i
n
-
1
h
o
u
r
s
p
en
t
is
e
−
1
.
1
4
9
2
−
(
−
1
.
6
9
8
5
)
=
1
.
732
.
Ho
wev
er
f
o
r
th
o
s
e
r
esp
o
n
d
en
ts
wh
o
h
a
v
e
s
ch
o
o
l
with
in
1
5
m
in
u
tes
is
−
0
.
5
7
4
9
5
−
0
.
6675
=
−
1
.
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=
0
.
2
8
8
6
7
tim
es
th
is
av
er
ag
e
wh
er
ea
s
th
o
s
e
wh
o
d
o
n
o
t
h
av
e
s
ch
o
o
ls
with
in
1
5
m
in
u
tes
+
0
.
5
7
4
9
5
+
0
.
6675
=
1
.
2425
=
3
.
46
tim
es
th
is
av
er
ag
e.
So
th
o
s
e
wh
o
h
av
e
s
ch
o
o
ls
m
o
r
e
th
a
n
1
5
m
in
u
tes
awa
y
ar
e
3
.
4
6
tim
es
(
th
e
av
e
r
ag
e
)
m
o
r
e
lik
ely
to
s
p
en
d
0
tim
e
in
t
h
e
co
llectio
n
o
f
f
ir
ewo
o
d
.
So
th
e
o
d
d
s
o
f
a
h
o
u
s
eh
o
ld
n
o
t
h
av
in
g
s
ch
o
o
l
less
th
an
1
5
m
in
an
d
s
p
en
d
in
g
n
o
tim
e
in
th
e
co
llectio
n
o
f
f
i
r
ewo
o
d
v
e
r
s
u
s
s
p
en
d
in
g
4
5
m
in
-
1
h
o
u
r
is
12
/
214
2
/
214
=
6
=
1
.
732
∗
3
.
46
.
T
ab
le
6
.
Par
am
eter
s
o
f
th
e
m
o
d
el
P
a
r
a
me
t
e
r
Ti
me
sp
e
n
d
o
n
f
u
e
l
w
o
o
d
c
o
l
l
e
c
t
i
o
n
1
2
3
4
5
µ
̂
−
1
.
1492
−
0
.
2133
2
.
0
5
3
5
1
.
0
0
7
4
−
1
.
6985
1
−
0
.
57495
0
.
4123
0
.
4
1
2
1
-
0
.
0
9
2
4
0
.
6675
2
0
.
57495
−
0
.
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0
.
4
1
2
1
0
.
0
9
2
4
−
0
.
6675
Ho
wev
er
th
e
o
d
d
s
in
f
av
o
u
r
o
f
p
er
s
o
n
h
av
i
n
g
s
ch
o
o
l
less
th
an
1
5
m
in
u
tes
f
r
o
m
h
o
m
e
an
d
s
p
en
d
in
g
n
o
tim
e
in
t
h
e
co
llectio
n
o
f
f
i
r
ewo
o
d
v
er
s
u
s
s
p
en
d
in
g
4
5
m
in
-
1
h
o
u
r
is
0
.
5
.
T
h
e
o
d
d
s
o
f
n
o
t
h
av
i
n
g
s
ch
o
o
l
with
in
1
5
m
in
u
tes
an
d
s
p
en
d
in
g
n
o
tim
e
in
th
e
c
o
llectio
n
o
f
f
ir
ewo
o
d
1
2
tim
es
m
o
r
e
t
h
an
th
at
o
f
h
a
v
in
g
s
ch
o
o
l
with
1
5
m
in
u
tes.
So
th
e
d
ata
h
er
e
d
o
es
n
o
t
v
alid
ate
h
y
p
o
th
esis
1
.
T
h
e
d
etails
o
f
p
ar
am
eter
s
o
b
tain
ed
f
itti
n
g
p
o
ly
to
m
o
u
s
m
o
d
els ar
e
g
iv
en
in
T
a
b
le
7
.
T
h
e
im
p
ac
t
o
f
in
ter
r
elatio
n
s
h
i
p
b
etwe
en
th
ese
v
ar
iab
les g
iv
en
in
T
ab
le
7
is
s
u
m
m
ar
ize
d
in
te
r
m
s
o
f
o
d
d
s
r
atio
in
T
ab
le
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
R
o
le
o
f lo
ca
tio
n
o
f h
o
u
s
eh
o
l
d
a
n
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its
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mic
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y
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u
mp
tio
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(
Jy
o
ti U.
Dev
ko
ta
)
201
T
ab
le
7
.
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