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68
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w
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s
:
Data
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aly
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is
Data
v
is
u
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tio
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E
x
p
lo
r
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r
y
d
ata
a
n
aly
s
is
Per
f
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m
an
ce
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atasets
Vis
u
aliza
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m
eth
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T
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s
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p
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ss
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rticle
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e
CC B
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C
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A
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Su
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Dep
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g
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in
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W
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o
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g
Un
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s
ity
J
ay
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3
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,
R
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lic
o
f
Ko
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ea
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m
ail: su
r
en
d
er
@
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.
ac
.
k
r
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
f
ir
s
t
an
d
f
o
r
em
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s
t
s
tep
in
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r
esear
ch
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tu
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y
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to
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f
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m
e
x
p
lo
r
ato
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y
d
ata
a
n
aly
s
is
(
E
DA)
.
T
h
e
m
ain
in
te
n
t
o
f
p
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f
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r
m
in
g
E
DA
is
to
aim
at
o
b
tain
i
n
g
in
s
ig
h
ts
to
a
m
ax
im
u
m
ex
ten
t
in
v
o
lv
in
g
v
ar
i
o
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s
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eth
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d
s
.
Pre
cisely
,
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tec
h
n
iq
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es
a
r
e
p
u
t
in
t
o
u
s
e
b
ef
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r
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in
itializin
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a
s
tatis
tical
m
o
d
el
o
r
c
o
n
d
u
ctin
g
co
m
p
lex
an
aly
s
is
[
1
]
.
T
h
e
p
r
o
ce
d
u
r
e
g
e
n
er
ally
d
escr
ib
es
th
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d
ataset
in
a
v
is
u
a
l
f
o
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at
f
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ea
s
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k
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en
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k
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cr
iter
ia
o
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en
titi
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th
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s
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n
ess
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o
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ata
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ea
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ata,
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o
t
th
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in
ter
d
ep
en
d
en
ce
b
etwe
en
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et
s
[
2
]
.
Pr
o
d
u
ctiv
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u
s
e
o
f
v
is
u
alizin
g
s
u
p
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lies
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d
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f
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k
in
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g
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d
atasets
,
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n
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f
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tu
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es,
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ter
s
ec
tio
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s
,
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tr
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ct
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tr
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id
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p
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ew
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esig
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t
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d
well
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ized
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lan
s
.
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ce
,
a
p
ac
k
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o
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l
ativ
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d
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o
f
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m
an
s
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ex
te
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iv
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ca
p
ac
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s
to
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ag
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an
d
c
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p
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tatio
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al
p
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f
d
ata
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an
ag
em
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t
is
a
h
ig
h
s
co
r
e
p
o
in
t f
o
r
its
ac
ce
p
tan
ce
[
3
]
.
L
o
f
tu
s
[
4
]
aim
s
at
p
r
o
v
id
in
g
th
e
m
eth
o
d
s
o
f
E
DA
th
at
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n
v
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way
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o
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h
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n
ce
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th
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u
p
p
ly
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s
a
b
ase
to
m
ak
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d
ec
is
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s
.
T
h
ese
an
aly
s
es
r
an
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f
o
c
u
s
in
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o
n
th
e
v
ar
iab
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p
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th
at
in
v
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lv
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b
o
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ca
teg
o
r
ical
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d
q
u
an
titativ
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d
ata.
E
v
e
n
f
u
r
th
e
r
,
it
aid
s
u
s
in
r
ea
lizin
g
v
ar
ia
b
le
r
elatio
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s
h
ip
s
,
esp
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t
h
r
o
u
g
h
b
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th
ter
s
e
o
f
g
r
ap
h
ical
a
n
d
m
at
h
em
atica
l
p
lo
ts
an
d
co
r
r
elatio
n
s
.
Sh
r
ei
n
er
[
5
]
d
escr
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d
i
f
f
er
en
t
p
o
lls
,
an
aly
zin
g
th
e
m
eth
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d
s
o
f
f
if
th
-
g
r
ad
e,
eig
h
th
-
g
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ad
e,
an
d
h
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g
h
s
ch
o
o
l
s
tu
d
e
n
ts
’
d
ata
v
is
u
aliza
tio
n
m
eth
o
d
s
.
2
7
s
tu
d
en
ts
wer
e
ex
am
in
ed
a
n
d
en
q
u
ir
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d
to
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a
b
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t
a
q
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to
r
ical
wh
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t
u
s
ag
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o
f
t
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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T
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n
o
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I
SS
N:
2252
-
8
7
7
6
Da
ta
a
n
a
lysi
s
a
n
d
visu
a
liz
a
tio
n
o
n
tita
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ic
a
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s
tu
d
en
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ma
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(
S
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g
-
C
h
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m
)
69
wh
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in
v
o
lv
ed
d
ata
v
is
u
aliza
t
io
n
.
Ou
tco
m
e
s
f
r
o
m
q
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a
n
titativ
e
an
d
q
u
alitativ
e
an
aly
s
es
f
o
cu
s
o
n
th
e
ca
p
ac
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f
o
r
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v
is
u
aliza
tio
n
s
to
im
p
r
o
v
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r
ea
s
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n
in
g
[
6
]
.
T
h
e
s
tu
d
y
o
f
a
n
o
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-
lin
ea
r
m
ath
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atica
l m
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itatio
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s
p
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o
f
C
o
r
o
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av
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s
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is
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(
C
OVI
D
-
1
9
)
ex
is
ten
ce
is
an
aly
ze
d
in
r
e
f
er
en
ce
[
6
]
a
n
d
it
m
ak
es
ass
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m
p
tio
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th
at
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u
ca
tio
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d
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ev
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Nu
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f
o
r
m
e
d
b
ased
o
n
t
h
e
p
r
o
p
o
s
al
o
f
th
e
m
o
d
el.
T
h
e
ca
n
ce
llatio
n
r
a
n
g
e
o
f
a
vo
y
ag
e
d
ep
en
d
s
u
p
o
n
ch
an
g
es
in
way
s
o
f
s
tatis
tics
an
d
d
y
n
a
m
ics
,
th
e
b
o
o
k
in
g
f
ac
to
r
s
,
an
d
t
h
e
ca
s
es
th
at
in
f
l
u
en
ce
th
e
ca
n
ce
llatio
n
asp
ec
ts
ar
e
ch
ec
k
ed
an
d
v
er
if
ied
i
n
r
ef
er
en
ce
[
7
]
.
T
h
e
m
u
ltiv
ar
iate
m
o
d
el
an
d
t
ec
h
n
iq
u
es
o
f
s
tatis
tics
ar
e
d
etailed
in
r
ef
er
e
n
ce
[
8
]
an
d
it
s
tr
ess
es
th
e
h
ig
h
lig
h
t
o
f
d
ata
s
cien
ce
in
ed
u
ca
tio
n
an
d
co
m
m
e
r
cial
b
ac
k
g
r
o
u
n
d
s
.
Desim
o
n
i
an
d
Po
[
9
]
p
r
esen
ts
th
e
d
ee
p
d
is
co
v
er
y
of
th
e
b
eh
a
v
io
r
o
f
t
h
e
ar
t
o
f
to
o
ls
f
o
r
lin
k
e
d
d
ata
v
is
u
aliza
tio
n
.
I
t
f
o
c
u
s
es
o
n
p
r
o
v
id
in
g
d
atasets
in
th
e
f
o
r
m
o
f
g
r
a
p
h
s
o
r
in
f
o
r
m
atio
n
ch
o
s
en
b
y
a
u
s
er
b
as
ed
o
n
in
ter
est,
with
th
e
m
o
tiv
e
to
f
ea
tu
r
e
th
ei
r
an
aly
s
is
.
T
h
e
to
o
ls
o
f
v
is
u
aliza
tio
n
ar
e
ex
p
lain
ed
a
n
d
co
r
r
elate
d
b
ased
o
n
th
ei
r
b
en
e
f
its
an
d
f
ac
ilit
ies.
T
h
e
em
p
lo
y
m
en
t
o
f
lin
ea
r
an
d
n
o
n
lin
ea
r
m
et
h
o
d
s
to
v
is
u
alize
d
ata
in
a
l
o
wer
d
im
e
n
s
io
n
in
th
e
c
o
n
ce
p
t
o
f
E
DA,
th
e
s
u
itab
le
m
eth
o
d
is
t
h
e
o
n
e
th
at
s
h
o
wca
s
es
th
e
av
ailab
ilit
y
o
f
n
atu
r
al
clu
s
ter
s
h
id
d
en
in
th
e
s
ca
tter
p
lo
ts
p
r
esen
ted
in
r
ef
er
en
ce
[
1
0
]
.
San
k
ar
a
n
ar
ay
a
n
an
et
a
l
.
[
1
1
]
s
tu
d
ies
th
e
m
eth
o
d
s
o
f
v
is
u
aliza
tio
n
tech
n
iq
u
es
in
m
u
ltip
le
d
ata
c
o
n
ce
r
n
in
g
f
ac
ts
s
u
ch
as
th
e
a
r
r
iv
al
o
f
f
lig
h
ts
,
th
e
co
u
n
t
o
f
p
ass
en
g
er
s
,
b
o
o
th
s
,
tim
e,
an
d
p
atter
n
s
o
f
s
ea
s
o
n
s
.
T
h
is
p
ap
er
ex
am
in
es
th
e
co
r
r
elatio
n
b
e
twee
n
th
ese
air
p
o
r
t
s
co
r
r
esp
o
n
d
in
g
to
d
if
f
er
en
t
v
is
u
aliza
tio
n
.
T
h
is
w
o
r
k
ca
n
m
o
v
e
to
e
v
er
y
air
p
o
r
t
an
d
ca
n
f
u
r
th
er
d
ef
in
e
th
e
a
n
aly
tical
m
eth
o
d
s
to
f
o
r
ec
ast
waitin
g
tim
e.
T
h
e
ai
m
o
f
r
e
f
er
en
ce
[
1
2
]
is
to
co
n
tr
ib
u
te
a
s
u
m
m
ar
y
b
y
in
itializin
g
a
v
is
u
aliza
tio
n
f
o
r
n
o
m
in
ee
s
f
o
r
Aca
d
e
m
y
awa
r
d
s
o
f
th
e
y
ea
r
s
b
etwe
en
1
9
9
3
a
n
d
2
0
1
7
.
T
h
e
o
u
tco
m
e
is
f
iv
e
v
ar
io
u
s
d
ash
b
o
ar
d
s
ar
e
co
m
m
en
ce
d
to
d
is
p
lay
t
h
e
p
atter
n
s
ea
r
ch
f
o
r
th
e
p
ar
am
e
ter
s
an
d
th
e
Ho
lt
-
W
in
ter
s
ex
p
o
n
en
tial
ap
p
licatio
n
f
o
r
f
latten
in
g
p
r
ed
ictio
n
.
Sw
ee
tlin
an
d
S
au
d
ia
[
1
3
]
illu
s
tr
ates
E
DA
an
aly
zin
g
th
e
f
ac
to
r
s
:
n
o
ttin
g
h
am
p
r
o
g
n
o
s
tic
in
d
ex
,
th
e
o
v
er
all
s
u
r
v
iv
al
s
tatu
s
,
an
d
r
ela
p
s
e
f
r
ee
s
tatu
s
co
llectin
g
th
e
d
ata
f
r
o
m
th
e
m
etab
r
ic
b
r
ea
s
t
ca
n
ce
r
to
d
is
cu
s
s
th
e
r
at
e
o
f
s
u
r
v
iv
al
an
d
r
ec
u
r
r
e
n
ce
o
f
th
e
d
is
ea
s
e
am
id
s
t
v
ar
i
o
u
s
p
atien
ts
ag
ed
5
y
ea
r
s
an
d
1
0
y
ea
r
s
.
T
h
e
E
DA
is
ex
ec
u
ted
em
p
lo
y
in
g
th
e
to
o
ls
o
f
v
is
u
aliza
tio
n
an
d
th
e
r
ec
o
r
d
e
d
o
b
s
er
v
atio
n
s
ar
e
v
is
u
alize
d
en
ab
lin
g
a
p
p
r
o
p
r
iat
e
s
war
m
p
lo
ts
an
d
tab
les.
Data
a
n
aly
s
is
g
en
er
ally
d
ea
ls
with
th
e
in
s
p
ec
tio
n
o
f
d
ata,
th
e
tr
an
s
f
o
r
m
atio
n
o
f
th
e
d
at
aset
in
to
a
lo
wer
d
im
en
s
io
n
,
an
d
m
o
d
eli
n
g
it
to
g
ain
ef
f
ec
tiv
e
d
escr
ip
t
io
n
an
d
r
ea
s
o
n
in
g
an
d
to
m
a
k
e
a
co
n
clu
s
io
n
.
I
t
is
o
n
e
of
th
e
r
is
in
g
ar
ea
s
in
t
h
e
s
ec
to
r
o
f
b
u
s
in
ess
an
d
t
ec
h
n
o
lo
g
y
.
Data
v
is
u
aliza
tio
n
w
o
r
k
s
with
th
e
r
ep
r
esen
tatio
n
o
f
d
ata
i
n
th
e
f
o
r
m
o
f
a
g
r
ap
h
.
I
t
co
n
v
er
ts
th
e
r
aw
d
ata
p
r
esen
t
in
to
an
ap
p
r
o
ac
h
a
b
le
way
to
v
is
u
alize
an
d
r
ec
o
g
n
ize
th
e
t
r
en
d
s
.
I
t
u
s
es
v
is
u
al
to
o
ls
lik
e
ch
ar
ts
,
g
r
ap
h
s
,
m
ap
s
,
an
d
o
t
h
er
v
is
u
al
m
eth
o
d
s
.
A
b
r
ief
n
o
te
h
as
b
ee
n
p
r
o
v
i
d
ed
in
th
is
s
ec
tio
n
d
etailin
g
th
e
im
p
lem
en
tatio
n
o
f
d
ata
an
aly
s
is
an
d
d
ata
v
is
u
aliza
tio
n
,
p
er
f
o
r
m
ed
in
two
p
r
o
v
id
ed
d
ata
s
ets
f
r
o
m
Kag
g
le
n
am
ely
“
titan
i
c”
an
d
“stu
d
en
t
’
s
p
er
f
o
r
m
an
ce
”.
2.
DATA AN
AL
Y
SI
S A
ND
DA
T
A
VI
SUA
L
I
Z
AT
I
O
N
I
t
is
im
p
o
r
tan
t
f
o
r
an
a
n
aly
s
t
to
k
n
o
w
th
e
b
u
s
in
ess
d
ea
ls
an
d
p
r
o
b
lem
s
f
ac
ed
b
y
a
n
o
r
g
a
n
izatio
n
to
ex
p
lo
r
e
an
d
d
r
aw
m
ea
n
i
n
g
f
u
l
in
s
ig
h
ts
f
r
o
m
t
h
e
r
aw
d
ata.
T
h
er
e
a
r
e
f
o
u
r
ty
p
es
o
f
d
ata
an
aly
s
is
.
Descr
ip
tiv
e
d
ata
an
aly
s
is
:
d
escr
ip
tiv
e
an
aly
s
is
is
th
e
ty
p
e
o
f
an
aly
s
is
th
at
is
u
s
ed
to
o
b
tain
th
e
s
u
m
m
ar
y
o
f
th
e
s
et
o
f
d
at
a
p
o
in
ts
to
s
atis
f
y
ev
er
y
o
r
d
e
r
o
f
th
e
d
ata.
I
t
is
ca
teg
o
r
ized
as
o
n
e
o
f
th
e
p
r
o
m
in
en
t
m
et
h
o
d
s
o
f
co
n
d
u
ctin
g
s
tatis
t
ical
d
ata
an
aly
s
is
[
1
4
]
.
T
h
e
d
escr
ip
tiv
e
a
n
aly
s
is
d
r
aw
s
a
s
u
m
m
ar
y
f
o
r
t
h
e
s
p
lit
o
f
t
h
e
d
ata,
h
elp
s
to
s
p
o
t
o
u
tlier
s
,
an
d
h
elp
s
to
id
en
tif
y
s
im
ilar
ities
am
o
n
g
v
ar
iab
les,
a
llo
win
g
th
e
s
tatis
tical
an
aly
s
is
to
a
g
r
ea
ter
ex
ten
t.
T
h
e
d
escr
ip
tiv
e
u
s
es
a
g
g
r
e
g
atio
n
o
f
d
ata
a
n
d
task
s
r
elate
d
to
m
in
i
n
g
to
f
etch
r
esu
lts
b
ased
o
n
p
r
ev
io
u
s
d
at
a
.
Diag
n
o
s
tic
an
al
y
s
is
:
d
iag
n
o
s
ti
c
an
aly
s
is
an
aly
s
es
an
d
in
ter
p
r
ets
th
e
d
ata
d
ee
p
ly
to
id
en
tify
th
e
an
o
m
alies
th
at
ca
n
n
o
t b
e
f
u
lly
ex
p
lain
ed
b
y
c
u
r
r
en
t u
n
d
e
r
s
tan
d
in
g
.
I
t
lo
o
k
s
in
to
th
e
d
ata
f
o
r
p
r
ev
io
u
s
u
n
d
er
ly
in
g
p
atter
n
s
an
d
d
eter
m
in
es
th
e
ca
u
s
al
r
elatio
n
s
h
ip
s
b
etwe
en
p
at
ter
n
s
th
at
lead
to
an
o
m
alies.
T
h
is
s
p
ec
i
al
ty
p
e
o
f
an
aly
s
is
h
elp
s
to
u
n
d
er
s
tan
d
f
u
tu
r
e
e
v
en
ts
.
Pre
d
ictiv
e
an
al
y
s
is
:
t
h
e
p
r
ed
ictiv
e
m
o
d
el
e
m
p
lo
y
s
s
tatis
tic
s
an
d
th
e
s
o
lu
tio
n
s
ar
e
f
o
r
ec
asted
b
ase
d
o
n
f
u
tu
r
e
o
u
tco
m
es.
I
t
wo
r
k
s
with
o
p
tim
izatio
n
alg
o
r
ith
m
s
to
r
ea
ch
p
o
s
s
ib
le
s
o
lu
tio
n
s
o
n
f
ac
to
r
s
to
attain
f
in
er
o
u
tco
m
es
in
th
e
f
u
tu
r
e
[
1
5
]
.
I
t
c
o
m
es
u
p
with
b
ett
er
g
u
id
an
ce
o
n
th
e
ch
an
ce
s
f
o
r
b
etter
o
u
tp
u
ts
.
Pre
s
cr
ip
tiv
e
an
aly
s
is
:
p
r
escr
ip
tiv
e
an
aly
tics
h
as
th
e
to
p
ca
p
ac
it
y
to
d
ay
i
n
th
e
f
iel
d
o
f
an
aly
tics
th
at
d
eter
m
in
es
i
n
d
ev
elo
p
m
e
n
t
o
f
th
e
ar
ea
s
v
ia
p
r
ed
ictiv
e
an
d
d
escr
ip
tiv
e
a
n
aly
tics
.
I
t
aid
s
th
e
u
s
er
s
in
p
r
o
v
id
i
n
g
ef
f
ec
tiv
e
a
n
s
wer
s
to
th
e
ex
is
tin
g
p
r
o
b
lem
s
an
d
o
p
tin
g
f
o
r
th
e
b
est
ch
o
ice
am
o
n
g
v
ar
io
u
s
o
p
tio
n
s
[
1
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
4
,
No
.
1
,
A
p
r
il
20
2
5
:
68
-
7
6
70
2
.
1
.
Da
t
a
v
is
ua
liza
t
io
n t
ec
hn
iqu
es
I
t
as
s
is
ts
to
h
an
d
le
b
ig
d
ata,
p
r
esen
tin
g
co
m
p
ar
is
o
n
s
,
em
p
lo
y
in
g
t
h
e
r
e
q
u
ir
e
d
ty
p
e
o
f
c
h
ar
t,
u
tili
zin
g
d
if
f
er
en
t
p
alettes
in
g
r
a
p
h
s
,
a
n
d
also
f
its
ap
t
f
o
r
th
e
d
i
g
ital
ag
e.
Vis
u
aliza
tio
n
is
a
b
asic
s
tep
in
th
e
p
r
o
ce
d
u
r
e
o
f
s
cien
tific
d
is
co
v
er
y
,
alth
o
u
g
h
it
was
n
o
t p
o
p
u
lar
till
th
e
l
ate
8
0
’
s
u
n
til
it
was
f
itti
n
g
ly
a
cc
ep
ted
as
a
s
tag
e
i
n
th
e
ar
ea
o
f
r
esear
ch
[
1
7
]
.
G
ó
m
ez
-
R
o
m
er
o
et
a
l.
[
1
8
]
h
i
g
h
lig
h
ted
th
e
f
ac
to
r
th
at
v
is
u
aliza
tio
n
s
u
p
p
o
r
ts
im
p
o
r
tan
t
3
m
o
tiv
es:
(
i)
b
r
ief
ch
ec
k
in
g
o
f
m
o
d
els
in
s
im
u
latio
n
p
r
o
ce
s
s
;
(
ii)
f
ast
p
ac
e
p
r
o
d
u
ctio
n
o
f
o
b
tain
e
d
r
esu
lts
in
s
im
u
latio
n
;
an
d
(
iii)
ea
s
y
ac
ce
s
s
to
co
m
m
u
n
icate
to
th
e
en
d
-
u
s
er
.
I
n
th
e
f
ield
o
f
Data
Scien
ce
,
th
e
m
eth
o
d
o
f
v
is
u
aliza
tio
n
is
co
n
s
id
er
ed
a
v
alu
ab
le
ass
et
in
th
e
p
r
o
ce
s
s
o
f
ex
p
lo
r
in
g
an
d
p
r
esen
tin
g
th
e
d
if
f
er
en
t
lev
els
o
f
th
e
r
esu
lt;
i.e
.
,
at
th
e
in
itial
an
d
th
e
last
p
r
o
ce
d
u
r
e
o
f
d
ata
an
aly
s
is
.
Vis
u
ally
ex
p
lo
r
in
g
th
e
d
ata
is
s
p
ec
if
ically
f
r
u
itfu
l w
h
en
s
o
u
r
ce
d
ata
is
k
n
o
wn
less
an
d
th
e
g
o
als o
f
th
e
an
aly
s
is
ar
e
n
o
t p
r
o
v
id
ed
in
a
d
etailed
m
an
n
er
.
I
n
th
is
id
ea
p
r
ese
n
ted
,
v
is
u
al
d
ata
e
x
p
lo
r
atio
n
is
lo
o
k
ed
f
o
r
war
d
to
ac
ce
s
s
in
g
a
n
ea
s
y
s
tep
to
s
o
lv
e
h
y
p
o
th
esis
-
g
en
er
atio
n
p
r
o
ce
d
u
r
es,
in
wh
ich
th
e
y
ca
n
b
e
ac
c
ep
ted
o
r
n
eg
lecte
d
b
ased
o
n
v
i
s
u
als,
an
d
also
n
ew
o
n
e
s
c
a
n
b
e
i
n
t
r
o
d
u
c
e
d
[
1
9
]
.
T
h
e
v
i
s
u
a
l
i
z
a
t
i
o
n
t
e
c
h
n
i
q
u
e
i
s
p
o
w
e
r
f
u
l
a
s
i
t
e
x
p
l
o
r
e
s
t
h
e
d
a
t
a
w
i
t
h
p
r
e
s
e
n
t
a
b
l
e
a
n
d
i
n
t
e
r
p
r
e
t
a
b
l
e
r
e
s
u
l
t
s
.
I
t
v
i
s
u
a
l
i
z
e
s
p
h
e
n
o
m
e
n
a
t
h
a
t
c
a
n
n
o
t
b
e
o
b
s
e
r
v
e
d
d
i
r
e
c
t
l
y
.
V
a
r
i
o
u
s
v
i
s
u
a
l
i
z
a
t
i
o
n
m
e
t
h
o
d
s
i
n
c
l
u
d
e
b
a
r
c
h
a
r
t
s
,
c
a
r
t
o
g
r
a
m
s
,
d
o
t
d
i
s
t
r
i
b
u
t
i
o
n
m
a
p
s
,
b
u
l
l
e
t
g
r
a
p
h
s
,
s
c
a
t
t
e
r
p
l
o
t
s
,
a
r
e
a
c
h
a
r
t
s
,
a
n
d
b
u
b
b
l
e
c
l
o
u
d
s
.
Scatter
p
lo
ts
s
er
v
e
as
a
b
as
ic
m
eth
o
d
o
f
v
is
u
aliza
tio
n
.
T
h
e
av
ailab
le
to
o
ls
o
f
f
lex
ib
ilit
y
lead
to
th
e
u
s
ag
e
o
f
s
ca
tter
p
l
o
ts
in
v
a
r
io
u
s
co
n
tex
ts
o
f
ex
p
lo
r
atio
n
an
d
p
r
esen
tatio
n
.
Scatter
p
lo
ts
illu
s
tr
ate
ev
er
y
in
d
iv
id
u
al
o
b
ject
in
th
e
d
ata
with
a
p
o
in
t,
p
lac
ed
o
n
d
im
en
s
i
o
n
s
o
f
o
r
th
o
g
o
n
al
an
d
two
co
n
tin
u
o
u
s
p
o
in
ts
[
2
0
]
.
Scatter
p
lo
t
m
eth
o
d
s
h
av
e
b
e
en
em
p
lo
y
e
d
to
a
g
r
ea
ter
e
x
ten
t
to
p
r
esen
t
s
tatis
tical
g
r
ap
h
ics
an
d
to
ex
p
o
s
e
h
id
d
en
s
tr
u
ctu
r
es
in
th
e
m
u
lti
v
ar
iate
d
ataset.
T
h
ese
p
lo
ts
ar
e
id
en
tifie
d
as
o
n
e
o
f
th
e
f
in
es
t,
p
o
ly
m
o
r
p
h
ic,
a
n
d
co
m
m
o
n
l
y
b
en
ef
icial
m
eth
o
d
s
f
o
r
d
is
p
lay
in
g
p
air
wis
e
ax
es
o
f
co
r
r
elatio
n
s
tr
u
ctu
r
es
an
d
lo
w
-
d
im
en
s
io
n
p
atter
n
s
o
f
th
e
av
ailab
le
d
ata
alo
n
g
s
id
e
th
e
s
u
m
m
ar
y
f
o
r
a
lar
g
e
to
tal
o
f
d
ata
[
2
1
]
.
T
h
e
b
a
r
ch
ar
t
is
o
n
e
o
f
th
e
m
o
s
t
co
m
m
o
n
v
is
u
aliza
tio
n
m
eth
o
d
s
wh
ich
is
u
s
ed
to
p
lo
t
th
e
x
an
d
y
ax
es
in
th
e
f
o
r
m
o
f
a
g
r
ap
h
f
o
r
th
e
d
at
a
in
th
e
f
ash
io
n
o
f
co
m
p
a
r
is
o
n
o
f
th
e
n
u
m
er
ical
co
m
p
o
n
e
n
ts
.
I
t
ca
n
b
e
ac
ce
s
s
ed
o
n
ly
o
n
th
e
elu
cid
ated
d
ataset
an
d
is
ca
p
ab
le
o
f
d
etailin
g
t
h
e
b
asic
in
f
o
r
m
atio
n
[
2
2
]
.
H
is
to
g
r
am
s
ar
e
am
o
n
g
th
e
m
o
s
t
wid
ely
u
s
ed
d
ata
v
is
u
aliza
tio
n
m
eth
o
d
s
.
A
h
is
to
g
r
am
is
n
o
r
m
ally
r
e
f
er
r
ed
to
as
a
ch
ar
t
th
at
d
is
p
lay
s
n
u
m
er
ic
d
ata
in
r
an
g
es
[
2
3
]
.
I
t sh
o
ws th
e
n
u
m
b
er
o
f
ti
m
es a
r
esp
o
n
s
e
o
r
r
an
g
e
o
f
r
es
p
o
n
s
es o
cc
u
r
s
in
a
d
ata
s
et.
2
.
2
.
E
x
ec
utio
n
r
equirem
ent
s
Pan
d
as
is
o
n
e
o
f
th
e
m
o
s
t
ex
t
en
s
iv
ely
u
s
ed
lib
r
ar
ies
in
Data
Scien
ce
.
Pan
d
as(
p
d
)
is
an
o
p
en
-
s
o
u
r
ce
Py
th
o
n
lib
r
a
r
y
f
o
r
en
a
b
lin
g
m
an
ip
u
latio
n
a
n
d
a
n
aly
s
is
o
f
d
ata.
Nu
m
p
y
(
n
p
)
is
also
o
n
e
o
f
th
e
im
p
o
r
tan
t
lib
r
ar
ies
in
Py
th
o
n
.
I
t
is
u
s
ed
f
o
r
wo
r
k
in
g
with
ar
r
ay
s
,
lin
ea
r
alg
eb
r
a,
m
atr
ices,
an
d
Fo
u
r
i
er
tr
an
s
f
o
r
m
s
[
2
4
]
,
[
25]
.
Ma
tp
lo
tlib
(
p
lt)
an
d
s
ea
b
o
r
n
(
s
n
s
)
ar
e
u
tili
ze
d
f
o
r
g
r
a
p
h
ical
r
ep
r
esen
tatio
n
th
at
s
er
v
es
v
is
u
al
p
u
r
p
o
s
es.
T
h
ese
lib
r
ar
ies
ar
e
ac
ce
s
s
ed
to
v
is
u
alize
th
e
d
ata
in
th
e
f
o
r
m
o
f
g
r
ap
h
s
an
d
ch
ar
ts
to
p
r
esen
t
co
m
p
ar
is
o
n
s
.
Fro
m
th
e
m
en
tio
n
e
d
lib
r
ar
ies
,
th
er
e
ar
e
a
lo
t
o
f
f
u
n
ctio
n
s
ass
o
ciate
d
with
ex
ec
u
tio
n
[
2
6
]
.
So
m
e
o
f
th
em
in
clu
d
e
h
ea
d
(
)
wh
ich
is
u
s
ed
to
r
ep
r
esen
t
th
e
f
ir
s
t
f
iv
e
co
lu
m
n
s
o
f
th
e
d
ata.
tail(
)
r
e
p
r
esen
ts
th
e
last
f
iv
e
co
lu
m
n
s
o
f
th
e
d
ataset.
in
f
o
(
)
s
p
ec
if
ies
th
e
d
if
f
er
e
n
t
d
ata
t
y
p
es
u
s
ed
in
th
e
d
ata
s
et
an
d
s
p
e
cif
ies
th
e
o
r
d
e
r
an
d
n
am
e
o
f
th
e
co
lu
m
n
,
co
u
n
t
o
f
n
u
ll
an
d
n
o
n
-
n
u
ll
v
alu
es,
an
d
m
em
o
r
y
.
s
h
a
p
e
g
iv
es
th
e
co
u
n
t
o
f
th
e
n
u
m
b
er
o
f
r
o
ws
an
d
co
lu
m
n
s
o
f
t
h
e
d
ataset.
d
r
o
p
(
)
d
r
o
p
s
o
r
d
elete
s
s
p
e
cif
ic
co
lu
m
n
s
in
th
e
d
ataset.
s
ize
r
etu
r
n
s
th
e
s
ize.
.
n
d
im
r
etu
r
n
s
th
e
n
u
m
b
er
o
f
d
im
en
s
io
n
ar
r
ay
s
.
d
r
o
p
n
a(
)
d
r
o
p
s
th
e
n
u
ll
v
alu
es
wh
ile
f
illn
a
(
)
f
ills
n
u
ll
v
alu
es.
d
r
o
p
_
d
u
p
licates(
)
d
r
o
p
s
v
alu
e
s
th
at
ar
e
d
u
p
licated
.
T
h
er
e
a
r
e
o
th
er
f
u
n
ctio
n
alities
ex
p
lain
ed
alo
n
g
with
th
e
r
esu
lt o
b
tain
ed
.
3.
I
M
P
L
E
M
E
NT
A
T
I
O
N
O
F
D
AT
A
ANA
L
YS
I
S
T
h
e
f
u
n
ctio
n
s
u
s
ed
in
th
is
ce
ll
ar
e
is
.
n
u
ll()
.
s
u
m
(
)
in
o
r
d
er
to
f
in
d
th
e
s
u
m
o
f
n
u
ll
v
alu
es,
‘
s
o
r
t_
v
alu
es(a
s
ce
n
d
in
g
=False)
’
s
o
r
ts
th
e
v
alu
es
p
r
esen
t
in
d
at
a
in
d
escen
d
in
g
o
r
d
er
,
‘
co
n
ca
t(
)
’
p
e
r
f
o
r
m
s
co
n
ca
ten
atio
n
alo
n
g
t
h
e
a
x
is
,
h
er
e
‘
ax
is
=1
’
s
p
ec
if
ies
th
e
r
o
ws,
‘
k
ey
s
’
to
n
am
e
th
e
co
l
u
m
n
s
o
f
th
e
o
u
tp
u
t.
co
u
n
t(
)
is
em
p
lo
y
ed
in
o
r
d
e
r
t
o
d
eter
m
i
n
e
h
o
w
m
a
n
y
tim
es
a
p
ar
ticu
lar
elem
en
t
ap
p
ea
r
s
in
a
p
r
o
v
id
ed
lis
t
o
r
s
tr
in
g
o
f
v
alu
es,
f
u
n
ctio
n
h
ea
d
(
)
r
etu
r
n
s
th
e
to
p
f
iv
e
r
o
ws wh
er
ea
s
tail(
)
r
et
u
r
n
s
th
e
last
f
iv
e
r
o
ws.
r
o
u
n
d
(
)
is
an
in
b
u
ilt
f
u
n
ctio
n
th
at
r
etu
r
n
s
r
o
u
n
d
-
o
f
f
v
al
u
es
to
th
e
g
iv
en
d
ig
it
alo
n
g
with
th
e
f
lo
atin
g
-
p
o
in
t
n
u
m
b
er
,
if
n
o
d
ig
it
is
p
r
o
v
i
d
ed
it
r
o
u
n
d
s
o
f
f
to
th
e
n
ea
r
est
in
teg
er
[
2
7
]
,
[
28]
.
Her
e
all
th
ese
f
u
n
ctio
n
s
ar
e
em
p
lo
y
ed
to
f
ill
m
is
s
in
g
v
alu
es in
Fig
u
r
e
1.
As
m
is
s
in
g
v
alu
es
ar
e
f
ill
ed
i
n
th
e
ab
o
v
e
ce
ll,
‘
is
.
n
u
ll()
.
s
u
m
(
)
’
is
u
s
ed
to
f
in
d
if
th
er
e
ar
e
an
y
n
u
ll
v
alu
es
p
r
esen
t
in
t
h
e
d
ataset.
T
h
e
o
u
t
p
u
t
o
b
tain
ed
is
0
wh
ic
h
in
d
icate
s
th
er
e
a
r
e
n
o
n
u
ll
v
alu
es
in
Fig
u
r
e
2
.
I
n
Fig
u
r
e
3
,
co
r
r
(
)
is
em
p
lo
y
ed
t
o
p
er
f
o
r
m
a
p
air
wis
e
co
r
r
elatio
n
o
f
ex
is
tin
g
c
o
lu
m
n
s
i
n
th
e
d
ataset.
An
y
n
u
ll
v
alu
es
ar
e
au
to
m
atica
lly
d
r
o
p
p
ed
.
I
t
o
n
l
y
p
er
f
o
r
m
s
in
n
u
m
e
r
ic
co
lu
m
n
s
,
‘
m
eth
o
d
=p
ea
r
s
o
n
’
is
u
s
ed
to
o
b
tain
s
tan
d
ar
d
co
r
r
elatio
n
co
ef
f
icien
t
.
I
n
th
e
Pear
s
o
n
m
eth
o
d
,
o
n
ly
u
s
es
3
n
u
m
e
r
ical
v
alu
es
wh
er
e
0
is
f
o
r
n
o
co
r
r
elatio
n
,
1
f
o
r
p
o
s
itiv
e
co
r
r
elatio
n
in
to
tal,
an
d
-
1
f
o
r
t
h
e
n
eg
ativ
e
co
r
r
elatio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Da
ta
a
n
a
lysi
s
a
n
d
visu
a
liz
a
tio
n
o
n
tita
n
ic
a
n
d
s
tu
d
en
t’
s
p
erf
o
r
ma
n
ce
…
(
S
e
o
n
g
-
C
h
e
o
l Ki
m
)
71
‘
u
n
iq
u
e
(
)
’
r
etu
r
n
s
th
e
co
u
n
t
o
f
u
n
iq
u
e
v
alu
es,
h
e
r
e
th
e
co
u
n
t
o
f
u
n
i
q
u
e
v
alu
es
in
t
h
e
s
ex
f
ield
is
r
etu
r
n
ed
alo
n
g
with
its
d
atat
y
p
e
.
Th
e
m
en
tio
n
e
d
th
r
ee
f
u
n
ctio
n
s
,
m
in
(
)
,
m
ax
(
)
,
a
n
d
m
ea
n
(
)
ar
e
u
s
u
ally
em
p
lo
y
ed
t
o
co
u
n
t
m
ax
im
u
m
,
m
in
im
u
m
,
a
n
d
av
e
r
ag
e
v
al
u
e
s
.
I
n
Fig
u
r
e
4
,
to
f
in
d
th
e
s
u
r
v
iv
al
an
d
d
ea
th
r
ates
f
o
r
t
h
e
y
o
u
n
g
est
an
d
o
ld
est,
v
alu
e
‘
0
’
is
ass
ig
n
ed
as
th
e
in
it
ial
v
alu
e
f
o
r
s
u
r
v
iv
al
,
an
d
‘
1
’
is
ass
ig
n
ed
f
o
r
th
e
d
ea
th
r
ate.
T
h
e
f
u
n
ctio
n
m
in
(
)
is
u
s
ed
f
o
r
y
o
u
n
g
est
as
ag
e
is
less
wh
en
co
m
p
ar
ed
f
o
r
t
h
e
o
ld
est
,
wh
ich
is
ca
lcu
lated
u
s
in
g
th
e
m
ax
(
)
f
u
n
ctio
n
,
an
d
f
in
all
y
‘
.
f
o
r
m
at’
is
u
s
ed
to
h
an
d
le
co
m
p
lex
s
tr
in
g
s
f
o
r
m
attin
g
p
r
o
ce
s
s
in
a
more
ef
f
icien
t
w
ay
.
I
n
Fig
u
r
e
5
,
th
e
m
in
im
u
m
,
m
ax
im
u
m
,
an
d
a
v
er
ag
e
ag
e
f
o
r
th
e
o
ld
est
an
d
y
o
u
n
g
est o
n
-
b
o
ar
d
is
d
eter
m
in
ed
to
u
n
d
er
s
tan
d
t
h
e
p
atter
n
s
l
y
in
g
in
t
h
e
d
ataset
m
o
r
e
e
f
f
ici
en
tly
.
Fig
u
r
e
1
.
Fil
l m
is
s
in
g
v
alu
es
Fig
u
r
e
2
.
Su
m
o
f
Nu
ll
v
alu
es
Fig
u
r
e
3
.
C
o
r
r
elatio
n
Fig
u
r
e
4
.
Su
r
v
iv
al
an
d
d
ea
th
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
4
,
No
.
1
,
A
p
r
il
20
2
5
:
68
-
7
6
72
Fig
u
r
e
5
.
Min
im
u
m
,
m
a
x
im
u
m
,
an
d
a
v
er
ag
e
a
g
e
‘
d
escr
ib
e(
)
’
d
is
p
lay
s
th
e
n
u
m
b
er
o
f
v
alu
es
p
r
esen
t
in
th
e
co
l
u
m
n
,
c
o
u
n
t
o
f
u
n
iq
u
e
v
alu
es,
f
r
eq
u
e
n
cy
,
an
d
th
e
t
o
p
/r
ep
ea
t
ed
v
alu
e
i
n
th
e
co
l
u
m
n
,
f
illn
a(
)
f
ills
n
u
ll
v
alu
es
f
o
r
th
e
f
ield
ac
ce
s
s
ed
.
I
n
Fig
u
r
e
6
,
d
escr
ib
e(
)
is
u
s
ed
t
o
ac
ce
s
s
in
f
o
r
m
atio
n
ab
o
u
t
th
e
em
b
ar
k
co
lu
m
n
in
th
e
d
ataset
an
d
t
h
e
r
es
u
lt
s
h
o
ws
th
e
m
o
s
t
f
r
eq
u
e
n
tly
u
s
ed
v
alu
e
is
‘
s
’
,
s
o
th
e
s
am
e
v
alu
e
is
b
ein
g
em
p
lo
y
ed
to
f
ill
th
e
n
u
ll
v
alu
es
i
n
th
e
s
am
e
co
lu
m
n
.
‘
g
r
o
u
p
b
y
(
)
f
u
n
ctio
n
is
u
s
ed
t
o
s
p
lit
th
e
d
ata
in
to
g
r
o
u
p
s
b
ased
o
n
a
g
iv
e
n
co
n
d
itio
n
an
d
‘
r
ep
lace
(
)
’
is
an
in
b
u
ilt
f
u
n
ctio
n
wh
er
e
all
o
cc
u
r
r
en
ce
s
o
f
a
s
u
b
s
tr
in
g
ar
e
r
e
p
lace
d
with
a
n
o
th
e
r
o
r
n
ew
s
tr
in
g
an
d
r
etu
r
n
s
a
co
p
y
o
f
it.
I
n
Fig
u
r
e
7
,
th
e
in
i
tial
co
lu
m
n
v
alu
es
ar
e
b
ein
g
r
ep
lace
d
an
d
‘
in
p
lace
=T
r
u
e’
is
u
s
ed
to
m
o
d
if
y
th
e
d
ata
in
p
lace
as
it
r
etu
r
n
s
n
o
t
h
in
g
a
n
d
th
e
d
ata
f
r
am
e
is
b
e
in
g
u
p
d
ated
.
Fo
llo
wed
b
y
th
a
t
g
r
o
u
p
in
g
is
d
o
n
e
b
ased
o
n
ag
e
i
n
an
i
n
itial
co
l
u
m
n
w
h
er
e
m
e
an
(
)
is
co
n
s
id
e
r
ed
.
‘
ilo
c(
)
’
p
r
o
v
id
es
th
e
lo
c
atio
n
o
f
th
e
in
teg
er
b
ased
o
n
in
d
ex
i
n
g
.
I
n
g
en
er
al,
th
e
f
u
n
ctio
n
is
u
s
ed
wh
en
th
e
lab
el
o
f
th
e
in
d
ex
is
n
o
t
n
u
m
e
r
ic
o
r
in
ca
s
e
if
th
e
i
n
d
ex
lab
el
is
n
o
t
p
r
o
v
id
ed
.
I
n
F
ig
u
r
e
8
,
‘
:
’
s
y
m
b
o
lizes e
x
tr
ac
tin
g
all
r
o
ws with
in
d
ex
.
Fig
u
r
e
6
.
d
escr
ib
e(
)
,
f
illn
a(
)
Fig
u
r
e
7
.
r
e
p
lace
(
)
,
g
r
o
u
p
b
y
(
)
Fig
u
r
e
8
.
ilo
c
(
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Da
ta
a
n
a
lysi
s
a
n
d
visu
a
liz
a
tio
n
o
n
tita
n
ic
a
n
d
s
tu
d
en
t’
s
p
erf
o
r
ma
n
ce
…
(
S
e
o
n
g
-
C
h
e
o
l Ki
m
)
73
4.
I
M
P
L
E
M
E
NT
A
T
I
O
N
O
F
D
AT
A
VI
SUA
L
I
Z
A
T
I
O
N
‘
s
ize(
)
’
f
u
n
ctio
n
r
etu
r
n
s
th
e
s
ize
o
f
th
e
d
ata
f
r
a
m
e
wh
er
ea
s
‘
u
n
s
tack
(
)
’
f
u
n
ctio
n
is
u
s
ed
to
co
n
v
er
t
th
e
d
ataset
in
to
an
u
n
s
tack
ed
f
o
r
m
at
o
r
in
g
e
n
er
al
to
r
e
m
o
v
e
an
ex
is
tin
g
s
tack
.
I
n
‘
h
ea
tm
a
p
’
co
lo
r
s
ar
e
u
s
ed
t
o
r
ep
r
esen
t
th
e
v
alu
e
in
t
h
e
m
at
r
ix
,
s
ea
b
o
r
n
lib
r
ar
y
.
T
h
e
d
ar
k
er
s
h
ad
e
r
ep
r
esen
ts
th
e
co
m
m
o
n
v
alu
es
wh
ile
th
e
lig
h
ter
o
n
es
r
ep
r
esen
t
th
e
least
co
m
m
o
n
ac
tiv
ity
.
I
n
F
ig
u
r
e
9
,
th
e
d
ataset
is
d
iv
id
ed
b
ased
o
n
two
ex
is
tin
g
co
lu
m
n
s
with
a
r
etu
r
n
in
s
ize
o
f
th
e
d
ata
f
r
am
e
in
an
u
n
s
tack
ed
f
o
r
m
at,
f
o
llo
wed
b
y
a
h
ea
t
m
ap
p
lo
tted
.
‘
p
y
p
lo
t
’
r
ep
r
esen
ts
a
p
ie
-
ch
ar
t
wh
er
e
th
e
s
lice
s
wi
ll
b
e
o
r
d
er
ed
an
d
p
lo
tted
co
u
n
ter
-
clo
ck
wis
e.
I
n
F
ig
u
r
e
1
0
,
a
p
y
p
lo
t
is
p
lo
tted
f
o
r
e
x
is
tin
g
co
l
u
m
n
s
lik
e
s
tu
d
en
t’
s
g
en
d
er
,
eth
in
icity
,
lu
n
ch
,
an
d
p
a
r
en
tal
lev
e
l
o
f
e
d
u
ca
tio
n
ex
h
ib
itin
g
d
if
f
er
en
t
s
ty
les
an
d
v
ar
iatio
n
s
.
‘
b
a
r
p
lo
t’
d
is
p
lay
s
th
e
ce
n
tr
al
v
al
u
e
f
o
r
a
n
u
m
e
r
ical
v
ar
iab
le
u
s
in
g
th
e
h
ei
g
h
t
o
f
e
ac
h
r
ec
tan
g
le.
W
e
u
s
e
‘
k
war
g
s
’
with
d
o
u
b
le
s
tar
s
an
d
it
is
f
lex
ib
le
to
h
a
n
d
l
e
ar
g
u
m
en
ts
.
*
*
k
war
g
s
is
a
b
u
ilt
-
in
f
u
n
ctio
n
th
at
ta
k
es
a
n
y
n
u
m
b
e
r
o
f
k
ey
wo
r
d
ar
g
u
m
en
ts
.
‘
Key
w
o
r
d
ar
g
u
m
en
ts
’
ar
e
d
ec
lar
e
d
b
y
a
n
am
e
an
d
if
th
e
v
alu
e
is
n
o
t
p
r
o
v
id
e
d
it
tak
es
a
d
ef
au
lt
v
alu
e
an
d
it
will
n
o
t
p
r
o
d
u
ce
an
er
r
o
r
.
*
*
k
war
g
s
i
s
an
‘
em
p
ty
d
ictio
n
ar
y
’
.
E
ac
h
u
n
d
ef
in
e
d
ar
g
u
m
en
t
is
s
to
r
ed
as
k
ey
-
v
alu
e
p
air
.
I
n
Fig
u
r
e
11
,
b
ar
p
lo
ts
ac
ce
s
s
in
g
d
if
f
er
en
t
s
ty
les
an
d
en
a
b
lin
g
v
ar
io
u
s
f
u
n
ctio
n
s
ar
e
s
h
o
wn
,
w
h
ich
ar
e
p
lo
tted
f
o
r
d
if
f
er
en
t
co
l
u
m
n
s
in
th
e
d
atas
et
p
r
o
v
id
e
d
with
a
leg
en
d
to
u
n
d
er
s
tan
d
th
e
s
co
r
e
in
a
m
o
r
e
p
r
ec
is
e
f
ash
io
n
.
I
n
Fig
u
r
e
1
2
,
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,
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Evaluation Warning : The document was created with Spire.PDF for Python.
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