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Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١
Ch
ap
ter 1
Intro
du
ctio
n a
nd
Da
ta C
olle
ctio
n
Busin
ess S
tatis
tics:
A F
irst C
ou
rse
Fifth
Editio
n
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٢
Lea
rnin
g O
bje
ctiv
es
In th
is c
ha
pte
r yo
u le
arn
:
�H
ow
Sta
tistic
s is
use
d in
bu
sin
ess
�T
he
so
urc
es o
f da
ta u
se
d in
bu
sin
ess
�T
he
typ
es o
f da
ta u
se
d in
bu
sin
ess
�T
he
ba
sic
s o
f Mic
roso
ft Exce
l
�T
he
ba
sic
s o
f Min
itab
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٣
Why L
ea
rn S
tatis
tics?
So
yo
u a
re a
ble
to m
ake
be
tter s
en
se
of th
e
ub
iqu
itou
s u
se
of n
um
be
rs:
�B
usin
ess m
em
os
�B
usin
ess re
se
arc
h
�T
ech
nic
al re
po
rts
�T
ech
nic
al jo
urn
als
�N
ew
sp
ap
er a
rticle
s
�M
ag
azin
e a
rticle
s
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٤
What is
sta
tistic
s?
�A
bra
nch
of m
ath
em
atic
s ta
kin
g a
nd
tran
sfo
rmin
g n
um
be
rs in
to u
se
ful in
form
atio
n fo
r d
ecis
ion
ma
ke
rs
�M
eth
od
s fo
r pro
ce
ssin
g &
an
aly
zin
g n
um
be
rs
�M
eth
od
s fo
r he
lpin
g re
du
ce
the
un
ce
rtain
ty
inh
ere
nt in
de
cis
ion
ma
kin
g
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٥
Why S
tudy S
tatis
tics?
Decisio
n M
akers U
se Statistics T
o:
�P
resent an
d d
escribe b
usin
ess data an
d in
form
ation p
roperly
�D
raw co
nclu
sions ab
out larg
e gro
ups o
f indiv
iduals o
r items,
usin
g in
form
ation co
llected fro
m su
bsets o
f the in
div
iduals o
r
items.
�M
ake reliab
le forecasts ab
out a b
usin
ess activity
�Im
pro
ve b
usin
ess pro
cesses
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٦
Types o
f Sta
tistic
s
�S
tatis
tics
�
The b
ranch
of m
athem
atics that tran
sform
s data in
to
usefu
l info
rmatio
n fo
r decisio
n m
akers.
De
scrip
tive S
tatis
tics
Collectin
g, su
mm
arizing, an
d
describ
ing d
ata
Infe
ren
tial S
tatis
tics
Draw
ing co
nclu
sions an
d/o
r m
akin
g d
ecisions co
ncern
ing a
populatio
n b
ased o
nly
on sam
ple
data
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٧
Descrip
tive S
tatis
tics
�C
olle
ct d
ata
�e
.g., S
urv
ey
�P
resent d
ata
�e
.g., T
able
s a
nd
gra
ph
s
�C
ha
racte
rize
data
�e
.g., S
am
ple
mea
n =
iX
n
∑
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٨
Infe
rentia
l Sta
tistic
s
�E
stim
atio
n
�e.g
., Estim
ate
the p
opula
tion
mean w
eig
ht u
sin
g th
e s
am
ple
mean w
eig
ht
�H
yp
oth
esis
testin
g
�e.g
., Test th
e c
laim
that th
e
popula
tion m
ean w
eig
ht is
120
pounds
Dra
win
g c
on
clu
sio
ns a
bo
ut a
larg
e g
rou
p o
f in
div
idu
als
ba
sed
on
a s
ub
set o
f the
larg
e g
rou
p.
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٩
Basic
Vocabula
ry o
f Sta
tistic
s
VA
RIA
BL
EA
varia
ble is a ch
aracteristic of an
item o
r indiv
idual.
DA
TA
Data
are the d
ifferent v
alues asso
ciated w
ith a v
ariable.
OP
ER
AT
ION
AL
DE
FIN
ITIO
NS
Data v
alues are m
eanin
gless u
nless th
eir variab
les hav
e op
eratio
nal
defin
ition
s, univ
ersally accep
ted m
eanin
gs th
at are clear to all asso
ciated
with
an an
alysis.
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٠
Basic
Vocabula
ry o
f Sta
tistic
s
PO
PU
LA
TIO
NA
pop
ula
tion
consists o
f all the item
s or in
div
iduals ab
out w
hich
you w
ant to
draw
a conclu
sion.
SA
MP
LE
A sa
mp
le is the p
ortio
n o
f a populatio
n selected
for an
alysis.
PA
RA
ME
TE
RA
para
meter is a n
um
erical measu
re that d
escribes a ch
aracteristic
of a p
opulatio
n.
ST
AT
IST
ICA
statistic is a n
um
erical measu
re that d
escribes a ch
aracteristic of
a sample.
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١١
Popula
tion v
s. S
am
ple
Po
pu
latio
nS
am
ple
Measu
res used
to d
escribe th
e
populatio
n are called
para
meters
Measu
res com
puted
from
sample d
ata are called sta
tistics
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٢
Why C
olle
ct D
ata
?
�A
mark
eting
research an
alyst n
eeds to
assess the
effectiven
ess of a n
ew telev
ision ad
vertisem
ent.
�A
pharm
aceutical m
anu
facturer n
eeds to
determ
ine
wheth
er a new
dru
g is m
ore effectiv
e than
those cu
rrently
in
use.
�A
n o
peratio
ns m
anag
er wan
ts to m
onito
r a man
ufactu
ring
pro
cess to fin
d o
ut w
heth
er the q
uality
of th
e pro
duct
bein
g m
anu
factured
is con
form
ing to
com
pan
y stan
dard
s.
�A
n au
dito
r wan
ts to rev
iew th
e finan
cial transactio
ns o
f a co
mpan
y in
ord
er to d
etermin
e wheth
er the co
mp
any is in
co
mplian
ce with
gen
erally accep
ted acco
untin
g
prin
ciples.
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٣
Sourc
es o
f Data
�P
rimary
So
urces: T
he d
ata collecto
r is the o
ne u
sing th
e data
for an
alysis
�D
ata from
a political su
rvey
�D
ata collected
from
an ex
perim
ent
�O
bserv
ed d
ata
�S
econdary
Sou
rces: The p
erson p
erform
ing d
ata analy
sis is
not th
e data co
llector
�A
naly
zing cen
sus d
ata
�E
xam
inin
g d
ata from
prin
t journ
als or d
ata publish
ed o
n th
e intern
et.
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٤
Sourc
es o
f data
fall in
to fo
ur
cate
gorie
s
�D
ata
dis
tribu
ted
by a
n o
rga
niz
atio
n o
r an
ind
ivid
ua
l
�A
de
sig
ne
d e
xp
erim
en
t
�A
su
rve
y
�A
n o
bse
rva
tion
al s
tud
y
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٥
Types o
f Varia
ble
s
�C
ateg
orica
l(q
ualitativ
e) variab
les hav
e valu
es that
can o
nly
be p
laced in
to categ
ories, su
ch as “y
es”an
d
“no
.”
�N
um
erical
(qu
antitativ
e) variab
les hav
e valu
es that
represen
t qu
antities.
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٦
Types o
f Da
ta
Da
ta
Ca
teg
oric
al
Nu
meric
al
Dis
cre
teC
on
tinu
ou
s
Exa
mp
les:
�M
arita
l Sta
tus
�P
olitic
al P
arty
�E
ye
Co
lor
(Defin
ed
ca
teg
orie
s)
Exa
mp
les:
�N
um
be
r of C
hild
ren
�D
efe
cts
pe
r ho
ur
(Co
un
ted
item
s)
Exa
mp
les:
�W
eig
ht
�V
olta
ge
(Measu
red
ch
ara
cte
ristic
s)
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٧
Pers
onal C
om
pute
r Pro
gra
ms
Used F
or S
tatis
tics
�M
inita
b
�A
sta
tistic
al p
ackage to
perfo
rm s
tatis
tical a
naly
sis
�D
esig
ned to
perfo
rm a
naly
sis
as a
ccura
tely
as p
ossib
le
�M
icro
soft E
xcel
�A
multi-fu
nctio
nal d
ata
analy
sis
tool
�C
an p
erfo
rm m
any fu
nctio
ns b
ut n
one a
s w
ell a
s p
rogra
ms th
at
are
dedic
ate
d to
a s
ingle
functio
n.
�B
oth
Min
itab a
nd E
xcel u
se w
ork
sh
eets
to s
tore
data
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٨
Min
itab &
Mic
rosoft E
xcel T
erm
s
�W
hen
you u
se Min
itab o
r Micro
soft E
xcel, y
ou p
lace the d
ata you
hav
e collected
in w
ork
sheets.
�T
he in
tersections o
f the co
lum
ns an
d ro
ws o
f work
sheets fo
rm
boxes called
cells.
�If y
ou w
ant to
refer to a g
roup o
f cells that fo
rms a co
ntig
uous
rectangular area, y
ou can
use a cell ra
nge.
�W
ork
sheets ex
ist insid
e a work
boo
k in
Excel a
nd
insid
e a
Pro
ject in M
inita
b.
�B
oth
work
sheets an
d p
rojects can
contain
both
data, su
mm
aries, an
d ch
arts.
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-١٩
You a
re u
sin
g p
rogra
ms p
roperly
if you c
an
�U
nd
ers
tan
d h
ow
to o
pera
te th
e p
rogra
m
�U
nd
ers
tan
d th
e u
nd
erly
ing s
tatis
tica
l co
nce
pts
�U
nd
ers
tan
d h
ow
to o
rga
niz
e a
nd p
rese
nt in
form
atio
n
�K
no
w h
ow
to re
vie
w re
su
lts fo
r erro
rs
�M
ake s
ecure
an
d c
learly
na
me
d b
acku
ps o
f yo
ur w
ork
Busin
ess S
tatis
tics: A
Firs
t Cours
e, 5
e ©
2009 P
rentic
e-H
all, In
c.
Chap 1
-٢٠
Chapte
r Su
mm
ary
�R
eview
ed w
hy a m
anag
er need
s to k
now
statistics
�In
troduced
key
defin
itions:
�P
op
ulatio
n v
s. Sam
ple
�P
rimary
vs. S
econ
dary
data ty
pes
�C
atego
rical vs. N
um
erical data
�E
xam
ined
descrip
tive v
s. inferen
tial statistics
�R
eview
ed d
ata types
�D
iscussed
Min
itab an
d M
icroso
ft Excel term
s
In th
is chap
ter, we h
ave
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