商务统计学课件英文版BSFC7e-CH01

Defining

andCollecting

DataChapter

1Chapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.ObjectivesChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.In

this

chapter

you

learn:

To

understand

issues

that

arise

when

definingvariables.How

to

define

variablesHow

to

collect

dataTo

identify

different

ways

to

collect

a

sampleUnderstand

the

types

of

survey

errorsClassifying

Variables

By

TypeChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVA

Categorical

(qualitative)

variables

take

categas

their

values

such

as

“yes”,

“no”,

or

“blue“green”.

Numerical

(quantitative)

variables

have

valuesrepresent

a

counted

or

measured

quantity.Discrete

variables

arise

from

a

counting

processContinuous

variables

arise

from

a

measuring

procesExamples

of

Types

of

VariablesDCOVADo

you

have

a

Facebookprofile?Yes

or

NoCategorical

(Qualitative)How

many

text

messageshaveyou

sent

in

the

pastthree

days?---------------Numerical(discrete)How

long

did

the

mobileapp

update

take

todownload?---------------Numerical(continuous)Chapter

1,

Slide

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2010

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Education,

Inc.QuestionResponsesVariable

TypeTypes

of

VariablesVariablesCategoricalNumericalDiscreteContinuousChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Examples:Marital

StatusPolitical

PartyEye

Color(Defined

categories)Examples:Number

of

Children

Defects

per

hour(Counted

items)Examples:WeightVoltage(Measured

characteristics)DCOVACollecting

Data

Correctly

Is

A

CriticalChapter

1,

Slide

1Copyright©

2016,

2013,

2010

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Education,

Inc.Task

Need

to

avoid

data

flawed

by

biases,ambiguities,

or

other

types

of

errors.

Results

from

flawed

data

will

be

suspect

oerror.

Even

the

most

sophisticated

statisticalmethods

are

not

very

useful

when

the

data

iflawed.DCOVADeveloping

Operational

Definitions

Is

CrucialTo

AvoidConfusion

/

ErrorsDCOVA

An

operational

definition

is

a

clear

and

precisestatement

that

provides

a

commonunderstanding

of

meaning

In

the

absence

of

an

operational

definitionmiscommunications

and

errors

are

likely

tooccur.

Arriving

at

operational

definition(s)

is

a

key

pof

the

Define

step

of

DCOVAChapter

1,

Slide

1Copyright©

2016,

2013,

2010

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Education,

Inc.Establishing

A

Business

ObjectiveFocuses

Data

CollectionChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Examples

Of

Business

Objectives:

A

marketing

research

analyst

needs

to

assess

the

effect

of

a

new

television

advertisement.

A

pharmaceutical

manufacturer

needs

to

determine

wheth

new

drug

is

more

effective

than

those

currently

in

use.

An

operations

manager

wants

to

monitor

a

manufacturingprocess

to

find

out

whether

the

quality

of

the

product

bmanufactured

is

conforming

to

company

standards.

An

auditor

wants

to

review

the

financial

transactions

ocompany

in

order

to

determine

whether

the

company

is

incompliance

with

generally

accepted

accounting

principlDCOVASources

of

DataChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVA

Primary

Sources:

The

data

collector

is

the

one

using

tfor

analysisData

from

a

political

surveyData

collected

from

an

experimentObserved

data

Secondary

Sources:

The

person

performing

data

analysinot

the

data

collectorAnalyzing

census

dataExamining

data

from

print

journals

or

data

published

on

the

inteSources

of

data

fall

into

fivecategoriesChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.

Data

distributed

by

an

organization

or

anindividualThe

outcomes

of

a

designed

experimentThe

responsesfrom

a

survey

The

results

of

conducting

an

observationalstudyData

collected

by

ongoing

business

activitiesDCOVAExamples

Of

Data

Distributed

ByOrganizations

or

IndividualsChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVA

Financial

data

on

a

company

provided

byinvestment

services.

Industry

or

market

data

from

market

researchfirms

and

trade

associations.

Stock

prices,

weather

conditions,

and

sportsstatistics

in

daily

newspapers.Examples

of

Data

From

ADesigned

ExperimentChapter

1,

Slide

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2016,

2013,

2010

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Education,

Inc.

Consumer

testing

of

different

versions

of

aproduct

to

help

determine

which

product

shouldbe

pursued

further.

Material

testing

to

determine

which

supplier’smaterial

should

be

used

in

a

product. Market

testing

on

alternative

productpromotions

to

determine

which

promotion

touse

more

broadly.DCOVAExamples

of

Survey

DataChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVA

A

survey

asking

people

which

laundry

detergenthas

the

best

stain-removing

abilities

Political

polls

of

registered

voters

during

policampaigns.

People

being

surveyed

to

determine

theirsatisfaction

with

a

recent

product

or

serviceexperience.Examples

of

Data

CollectedFrom

Observational

StudiesChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.

Market

researchers

utilizing

focus

groups

toelicit

unstructured

responses

to

open-endedquestions.

Measuring

the

time

it

takes

for

customers

to

beserved

in

a

fast

food

establishment.

Measuring

the

volume

of

traffic

through

anintersection

to

determine

if

some

form

ofadvertising

at

the

intersection

is

justified.DCOVAExamples

of

Data

Collected

FromOngoing

Business

ActivitiesChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.

A

bank

studies

years

of

financial

transactions

thelp

them

identify

patterns

of

fraud.

Economists

utilize

data

on

searches

done

viaGoogle

to

help

forecast

future

economicconditions.

Marketing

companies

use

tracking

data

toevaluate

the

effectiveness

of

a

web

site.DCOVAData

Is

Collected

From

Either

APopulation

or

A

SampleChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVAPOPULATIONA

population

consists

of

all

the

items

orindividuals

about

which

you

want

to

draw

aconclusion.

The

population

is

the

“large

grouSAMPLEA

sample

is

the

portion

of

a

populationselected

for

analysis.

The

sample

is

the

“smalgroup”Population

vs.

SamplePopulationSampleAll

the

items

or

individuals

aboutwhich

you

want

to

draw

conclusion(s)A

portion

of

the

population

ofitems

or

individualsDCOVAChapter

1,

Slide

1Copyright©

2016,

2013,

2010

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Education,

Inc.Collecting

Data

Via

Sampling

Is

UsedChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.When

Selecting

A

Sample

Is

Less

time

consuming

than

selecting

every

itemin

the

population.

Less

costly

than

selecting

every

item

in

thepopulation.

Less

cumbersome

and

more

practical

thananalyzing

the

entire

population.DCOVAThings

To

Consider

/

Deal

With

InPotential

Sources

Of

DataDCOVAChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Is

the

source

of

data

structured

or

unstructuredHow

is

electronic

data

formatted?How

is

data

encoded?Structured

Data

Follows

An

OrganizingPrinciple

&

Unstructured

Data

Does

NotChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVAA

Stock

Ticker

Provides

Structured

Data:

The

stock

ticker

repeatedly

reports

a

company

name,

thenumber

of

shares

last

traded,

the

bid

price,

and

the

percentchange

in

the

stock

price.

Due

to

their

inherent

structure,

data

from

tables

andforms

are

structured

data.

E-mails

from

five

people

concerning

stock

trades

is

anexample

of

unstructured

data.

In

these

e-mails

you

cannot

count

on

the

informationbeingshared

in

a

specific

order

or

format.This

book

deals

exclusively

with

structured

dataAll

Of

The

MethodsIn

This

BookDeal

With

Structured

DataChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVA

To

use

the

techniques

in

this

bookonunstructured

data

you

need

to

convert

theunstructured

into

structured

data.

For

many

of

the

questions

you

might

want

toanswer,

the

starting

point

can

/

will

be

tabulardata.Data

Can

Be

Formatted and

/orEncoded

In

More

Than

One

WayChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVA

Some

electronic

formats

are

more

readilyusable

than

others.

Different

encodings

can

impact

the

precision

ofnumerical

variables

and

can

also

impact

datacompatibility.

As

you

identify

and

choose

sources

of

data

youneed

to

consider

/

deal

with

these

issuesData

Cleaning

Is

Often

A

NecessaryChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Activity

When

Collecting

DataOften

find

“irregularities”

in

the

dataTypographical

or

data

entry

errorsValues

that

are

impossible

or

undefinedMissing

valuesOutliers

When

found

these

irregularities

should

bereviewed

/

addressed

Both

Excel

&

Minitab

can

be

used

to

addressirregularitiesDCOVAAfter

Collection

It

Is

Often

Helpful

ToChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Recode

Some

Variables

Recoding

a

variable

can

either

supplement

or

replacethe

original

variable.

Recoding

a

categorical

variable

involves

redefiningcategories.

Recoding

a

quantitative

variable

involves

changing

thivariable

into

a

categorical

variable.

When

recoding

be

sure

that

the

new

categories

aremutually

exclusive

(categories

do

not

overlap)

andcollectively

exhaustive

(categories

cover

all

possiblvalues).DCOVAA

Sampling

Process

Begins

With

ASampling

FrameChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.

Thesampling

frame

is

a

listing

of

items

thatmake

up

the

population

Frames

are

data

sources

such

as

populationlists,

directories,

or

maps

Inaccurate

or

biased

results

can

result

if

aframe

excludes

certain

portions

of

thepopulation

Using

different

frames

to

generate

data

canlead

to

dissimilar

conclusionsDCOVATypes

of

SamplesSamplesNon-ProbabilitySamplesJudgmentProbability

SamplesSimpleRandomSystematicStratifiedClusterConvenienceDCOVAChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Types

of

Samples:Nonprobability

SampleChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.

In

a

nonprobability

sample,

items

included

arechosen

without

regard

to

their

probability

ofoccurrence.

In

convenience

sampling,

items

are

selected

basedonly

on

the

fact

that

they

are

easy,

inexpensive,

orconvenient

to

sample.

In

a

judgment

sample,you

get

the

opinions

of

pre-selected

experts

in

the

subject

matter.DCOVATypes

of

Samples:Probability

Sample

In

a

probability

sample,

items

in

the

sampleare

chosen

on

the

basis

of

known

probabilities.Probability

SamplesSimpleRandomSystematicStratifiedClusterChapter

1,

Slide

1Copyright©

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Education,

Inc.DCOVAProbability

Sample:Simple

Random

SampleChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.

Every

individual

or

item

from

the

frame

has

anequal

chance

of

being

selected

Selection

may

be

with

replacement

(selectedindividual

is

returned

to

frame

for

possiblereselection)

or

without

replacement

(selectedindividual

isn’t

returned

to

the

frame).

Samples

obtained

from

table

of

randomnumbers

or

computer

randomnumbergenerators.DCOVASelecting

a

Simple

Random

Sample

Using

ARandom

Number

TableSampling

Frame

ForPopulation

With

850ItemsItem

Name

Item

#Bev

R.Ulan

X.....Joann

P.Paul

F.001002....849850Portion

Of

A

Random

Number

Table492808892435779002838116307275111000234012860746979664489439098932399720048494208887208401The

First

5

Items

in

a

simplerandom

sampleItem

#

492Item

#

808Item

#

892

--

does

not

exist

so

ignoreItem

#

435Item

#

779Item

#

002Chapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVADecide

on

sample

size:

n

Divide

frame

of

N

individuals

into

groups

of

kindividuals:

k=N/n

Randomly

select

one

individual

from

the

1stgroupSelect

every

kth

individual

thereafterProbability

Sample:Systematic

SampleN

=

40n

=

4k

=

10First

GroupDCOVAChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Probability

Sample:Stratified

Sample

Divide

population

into

two

or

more

subgroups(called

strata)

accorto

some

common

characteristic

A

simple

random

sample

is

selected

from

each

subgroup,

with

samplesizes

proportional

to

strata

sizesSamples

from

subgroups

are

combined

into

one

This

is

a

common

technique

when

sampling

population

of

voters,stratifying

across

racial

or

socio-economic

lines.PopulationDividedinto

4strataDCOVAChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Probability

SampleCluster

Sample

Population

is

divided

into

several

“clusters,”

each

representatithe

populationA

simple

random

sample

of

clusters

is

selected

All

items

in

the

selected

clusters

can

be

used,

or

items

can

bechosen

fromacluster

using

another

probabilitysamplingtechnique

A

common

application

of

cluster

sampling

involves

election

exit

powhere

certain

election

districts

are

selected

and

sampled.Populationdivided

into16

clusters.Randomly

selectedclusters

for

sampleChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.DCOVAProbability

Sample:Comparing

Sampling

MethodsChapter

1,

Slide

1Copyright©

2016,

2013,

2010

Pearson

Education,

Inc.Simple

random

sample

and

Systematic

sampleSimple

to

use

May

not

be

a

good

representation

of

the

population’sunderlying

characteristicsStratified

sample

Ensures

representation

of

individuals

acr