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Ch.+12-BUAD+2070-001+(2)
Course: BUAD 2070, Spring 2012School: Toledo
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by John Loucks St. Slides Edwards University 2011CengageLearning.AllRightsReserved.Maynotbescanned,copied orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart. Slide 1 Chapter12 TestsofGoodnessofFitandIndependence GoodnessofFitTest:AMultinomialPopulation TestofIndependence s GoodnessofFitTest: PoissonandNormalDistributions 2011CengageLearning.AllRightsReserved.Maynotbescanned,copied...
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by
John
Loucks
St. Slides Edwards
University
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
1
Chapter12
TestsofGoodnessofFitandIndependence
GoodnessofFitTest:AMultinomialPopulation
TestofIndependence
s
GoodnessofFitTest:
PoissonandNormalDistributions
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
2
Hypothesis(GoodnessofFit)Test
forProportionsofaMultinomialPopulation
1.Statethenullandalternativehypotheses.
H0:Thepopulationfollowsamultinomial
0
distributionwithspecifiedprobabilities
foreachofthekcategories
Haa:Thepopulationdoesnotfollowa
multinomialdistributionwithspecified
probabilitiesforeachofthekcategories
k
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
3
Hypothesis(GoodnessofFit)Test
forProportionsofaMultinomialPopulation
2.Selectarandomsampleandrecordtheobserved
frequency,fi,foreachofthekcategories.
3.AssumingH0istrue,computetheexpected
frequency,ei,ineachcategorybymultiplyingthe
categoryprobabilitybythesamplesize.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
4
Hypothesis(GoodnessofFit)Test
forProportionsofaMultinomialPopulation
4.Computethevalueoftheteststatistic.
( f i ei ) 2
2 =
ei
i =1
k
where:
fi=observedfrequencyforcategoryi
ei=expectedfrequencyforcategoryi
k=numberofcategories
Note:Theteststatistichasachisquaredistribution
withk1dfprovidedthattheexpectedfrequencies
are5ormoreforallcategories.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
5
Hypothesis(GoodnessofFit)Test
forProportionsofaMultinomialPopulation
5.Rejectionrule:
pvalueapproach:
RejectH0ifpvalue<
Criticalvalueapproach:
RejectH0if
2
2
whereisthesignificanceleveland
therearek1degreesoffreedom
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
6
MultinomialDistributionGoodnessofFitTest
sExample:FingerLakesHomes(A)
FingerLakesHomesmanufacturesfourmodelsof
prefabricatedhomes,atwostorycolonial,alogcabin,
asplitlevel,andanAframe.Tohelpinproduction
planning,managementwouldliketodetermineif
previouscustomerpurchasesindicatethatthereisa
preferenceinthestyleselected.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
7
MultinomialDistributionGoodnessofFitTest
sExample:FingerLakesHomes(A)
Thenumberofhomessoldofeachmodelfor100
salesoverthepasttwoyearsisshownbelow.
SplitA
ModelColonialLogLevelFrame
#Sold30203515
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
8
MultinomialDistributionGoodnessofFitTest
s
Hypotheses
H0:pC=pL=pS=pA=.25
Ha:Thepopulationproportionsarenot
pC=.25,pL=.25,pS=.25,andpA=.25
where:
pC=populationproportionthatpurchaseacolonial
pL=populationproportionthatpurchasealogcabin
pS=populationproportionthatpurchaseasplitlevel
pA=populationproportionthatpurchaseanAframe
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
9
MultinomialDistributionGoodnessofFitTest
s
RejectionRule
RejectH0ifpvalue<.05or2>7.815.
With=.05and
k1=41=3
degreesoffreedom
DoNotRejectH0
RejectH0
7.815
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
2
Slide
10
MultinomialDistributionGoodnessofFitTest
s
ExpectedFrequencies
e1=.25(100)=25e2=.25(100)=25
e3=.25(100)=25e4=.25(100)=25
s
TestStatistic
( 30 25) 2 ( 20 25) 2 ( 35 25) 2 (15 25) 2
2 =
+
+
+
25
25
25
25
=1+1+4+4
=10
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
11
MultinomialDistributionGoodnessofFitTest
s
ConclusionUsingthepValueApproach
AreainUpperTail.10.05.025.01.005
2Value(df=3)6.2517.8159.34811.34512.838
Because2=10isbetween9.348and11.345,the
areaintheuppertailofthedistributionisbetween
.025and.01.
Thepvalue<.Wecanrejectthenullhypothesis.
Actualpvalueis.0186
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
12
MultinomialDistributionGoodnessofFitTest
s
ConclusionUsingtheCriticalValueApproach
2=10>7.815
Wereject,atthe.05levelofsignificance,
theassumptionthatthereisnohomestyle
preference.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
13
TestofIndependence:ContingencyTables
1.Setupthenullandalternativehypotheses.
H0:Thecolumnvariableisindependentof
0
therowvariable
Haa:Thecolumnvariableisnotindependent
oftherowvariable
2.Selectarandomsampleandrecordtheobserved
frequency,fij,foreachcellofthecontingencytable.
3.Computetheexpectedfrequency,eij,foreachcell.
( Row i Total ) ( Column j Total )
eij =
Sample Size
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
14
TestofIndependence:ContingencyTables
4.Computetheteststatistic.
2 =
i
( f ij eij ) 2
j
eij
5.Determinetherejectionrule.
RejectH0ifpvalue<or.
2
2
whereisthesignificanceleveland,
withnrowsandmcolumns,thereare
(n1)(m1)degreesoffreedom.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
15
ContingencyTable(Independence)Test
sExample:FingerLakesHomes(B)
EachhomesoldbyFingerLakesHomescanbe
classifiedaccordingtopriceandtostyle.Finger
Lakesmanagerwouldliketodetermineifthe
priceofthehomeandthestyleofthehomeare
independentvariables.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
16
ContingencyTable(Independence)Test
sExample:FingerLakesHomes(B)
Thenumberofhomessoldforeachmodeland
priceforthepasttwoyearsisshownbelow.For
convenience,thepriceofthehomeislistedaseither
$99,000orlessormorethan$99,000.
PriceColonialLogSplitLevelAFrame
<$99,00018
61912
>$99,0001214 163
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
17
ContingencyTable(Independence)Test
s
Hypotheses
H0:Priceofthehomeisindependentofthe
styleofthehomethatispurchased
Ha:Priceofthehomeisnotindependentofthe
styleofthehomethatispurchased
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
18
ContingencyTable(Independence)Test
s
ExpectedFrequencies
PriceColonialLogSplitLevelAFrameTotal
<$99K
186191255
>$99K
12 1416345
Total
30203515100
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
19
ContingencyTable(Independence)Test
s
RejectionRule
2
With=.05and(21)(41)=3d.f., .05 = 7.815
RejectH0ifpvalue<.05or2>7.815
s
TestStatistic
(18 16. 5) 2 ( 6 11) 2
( 3 6. 75) 2
2 =
+
+ ... +
16.5
11
6. 75
=.1364+2.2727+...+2.0833=9.149
=.1364+2.2727+...+2.0833=9.149
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
20
ContingencyTable(Independence)Test
s
ConclusionUsingthepValueApproach
AreainUpperTail.10.05.025.01.005
2Value(df=3)6.2517.8159.34811.34512.838
Because2=9.145isbetween7.815and9.348,the
areaintheuppertailofthedistributionisbetween
.05and.025.
Thepvalue<.Wecanrejectthenullhypothesis.
Actualpvalueis.0274
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
21
ContingencyTable(Independence)Test
s
ConclusionUsingtheCriticalValueApproach
2=9.145>7.815
Wereject,atthe.05levelofsignificance,
theassumptionthatthepriceofthehomeis
independentofthestyleofhomethatis
purchased.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
22
GoodnessofFitTest:PoissonDistribution
1.Statethenullandalternativehypotheses.
H0:ThepopulationhasaPoissondistribution
0
Haa:ThepopulationdoesnothaveaPoissondistribution
:ThepopulationdoesnothaveaPoissondistribution
2.Selectarandomsampleand
a.Recordtheobservedfrequencyfiforeachvalueof
thePoissonrandomvariable.
b.Computethemeannumberofoccurrences.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
23
GoodnessofFitTest:PoissonDistribution
3.Computetheexpectedfrequencyofoccurrencesei
foreachvalueofthePoissonrandomvariable.
MultiplythesamplesizebythePoissonprobability
ofoccurrenceforeachvalueofthePoissonrandom
variable.
Iftherearefewerthanfiveexpectedoccurrences
forsomevalues,combineadjacentvaluesand
reducethenumberofcategoriesasnecessary.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
24
GoodnessofFitTest:PoissonDistribution
4.Computethevalueoftheteststatistic.
( f i ei ) 2
2 =
ei
i =1
k
where:
fi=observedfrequencyforcategoryi
ei=expectedfrequencyforcategoryi
k=numberofcategories
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
25
GoodnessofFitTest:PoissonDistribution
5.Rejectionrule:
pvalueapproach:
RejectH0ifpvalue<
Criticalvalueapproach:
RejectH0if
2
2
whereisthesignificanceleveland
therearek2degreesoffreedom
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
26
GoodnessofFitTest:PoissonDistribution
s
Example:TroyParkingGarage
Instudyingtheneedforanadditionalentranceto
acityparkinggarage,aconsultanthasrecommended
ananalysisapproachthatisapplicableonlyin
situationswherethenumberofcarsenteringduringa
specifiedtimeperiodfollowsaPoissondistribution.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
27
GoodnessofFitTest:PoissonDistribution
s
Example:TroyParkingGarage
Arandomsampleof100oneminutetimeintervals
resultedinthecustomerarrivalslistedbelow.A
statisticaltestmustbeconductedtoseeifthe
assumptionofaPoissondistributionisreasonable.
#Arrivals0123456789101112
Frequency014101420121298631
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
28
GoodnessofFitTest:PoissonDistribution
s
Hypotheses
H0:Numberofcarsenteringthegarageduring
aoneminuteintervalisPoissondistributed
Ha:Numberofcarsenteringthegarageduringa
oneminuteintervalisnotPoissondistributed
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
29
GoodnessofFitTest:PoissonDistribution
s
EstimateofPoissonProbabilityFunction
otalArrivals=0(0)+1(1)+2(4)+...+12(1)=600
TotalTimePeriods=100
Estimateof=600/100=6
Hence,
6 x e 6
f ( x) =
x!
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
30
GoodnessofFitTest:PoissonDistribution
s
ExpectedFrequencies
xf(x)nf(x)
0
1
2
3
4
5
6
.0025
.0149
.0446
.0892
.1339
.1606
.1606
.25
1.49
4.46
8.92
13.39
16.06
16.06
xf(x) nf(x)
7
8
9
10
11
12+
Total
.1377
.1033
.0688
.0413
.0225
.0201
1.0000
13.77
10.33
6.88
4.13
2.25
2.01
100.00
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
31
GoodnessofFitTest:PoissonDistribution
s
ObservedandExpectedFrequencies
ifiei
0or1or2
3
4
5
6
7
8
9
10ormore
5
10
14
20
12
12
9
8
10
fiei
6.20
8.92
13.39
16.06
16.06
13.77
10.33
6.88
8.39
1.20
1.08
0.61
3.94
4.06
1.77
1.33
1.12
1.61
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
32
GoodnessofFitTest:PoissonDistribution
s
RejectionRule
With=.05andkp1=911=7d.f.
(wherek=numberofcategoriesandp=number
2
ofpopulationparametersestimated), .05 = 14.067
RejectH0ifpvalue<.05or2>14.067.
s
TestStatistic
( 1.20)2 (1.08)2
(1.61)2
2 =
+
+ ... +
= 3.268
6.20
8.92
8.39
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
33
GoodnessofFitTest:PoissonDistribution
s
ConclusionUsingthepValueApproach
AreainUpperTail.90.10.05.025.01
2Value(df=7)2.83312.01714.06716.01318.475
Because2=3.268isbetween2.833and12.017inthe
ChiSquareDistributionTable,theareaintheuppertail
ofthedistributionisbetween.90and.10.
Thepvalue>.Wecannotrejectthenullhypothesis.
ThereisnoreasontodoubttheassumptionofaPoisson
distribution.
Actualpvalueis.8591
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
34
GoodnessofFitTest:NormalDistribution
1.Statethenullandalternativehypotheses.
H0:Thepopulationhasanormaldistribution
0
Haa:Thepopulationdoesnothaveanormaldistribution
2.Selectarandomsampleand
a.Computethemeanandstandarddeviation.
b.Defineintervalsofvaluessothattheexpected
frequencyisatleast5foreachinterval.
c.Foreachinterval,recordtheobservedfrequencies
3. Computetheexpectedfrequency,ei,foreachinterval.
(Multiplythesamplesizebytheprobabilityofa
normalrandomvariablebeingintheinterval.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
35
GoodnessofFitTest:NormalDistribution
4.Computethevalueoftheteststatistic.
( f i ei ) 2
2 =
ei
i =1
k
5.RejectH0if 2 2 (whereisthesignificancelevel
andtherearek3degreesoffreedom).
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
36
GoodnessofFitTest:NormalDistribution
s
Example:IQComputers
IQComputers(onebetterthanHP?)manufactures
andsellsageneralpurposemicrocomputer.Aspart
ofastudytoevaluatesalespersonnel,management
wantstodetermine,ata.05significancelevel,ifthe
annualsalesvolume(numberofunitssoldbya
salesperson)followsanormalprobability
distribution.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
37
GoodnessofFitTest:NormalDistribution
s
Example:IQComputers
Asimplerandomsampleof30ofthesalespeople
wastakenandtheirnumbersofunitssoldarelisted
below.
33434445525256586364
64656668707273737475
8384858691929498102105
(mean=71,standarddeviation=18.54)
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
38
GoodnessofFitTest:NormalDistribution
s
Hypotheses
H0:Thepopulationofnumberofunitssold
hasanormaldistributionwithmean71
andstandarddeviation18.54.
Ha:Thepopulationofnumberofunitssold
doesnothaveanormaldistributionwith
mean71andstandarddeviation18.54.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
39
GoodnessofFitTest:NormalDistribution
s
IntervalDefinition
Tosatisfytherequirementofanexpected
frequencyofatleast5ineachintervalwewill
dividethenormaldistributioninto30/5=6
equalprobabilityintervals.
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
40
GoodnessofFitTest:NormalDistribution
s
IntervalDefinition
Areas
=1.00/6
=.1667
53.02
71
88.98=71+.97(18.54)
71.43(18.54)=63.03 78.97
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
41
GoodnessofFitTest:NormalDistribution
s
ObservedandExpectedFrequencies
ifieifiei
Lessthan53.02
53.02to63.03
63.03to71.00
71.00to78.97
78.97to88.98
Morethan88.98
Total
6
3
6
5
4
6
30
5
5
5
5
5
5
30
1
2
1
0
1
1
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
42
GoodnessofFitTest:NormalDistribution
s
RejectionRule
With=.05andkp1=621=3d.f.
(wherek=numberofcategoriesandp=number
2
ofpopulationparametersestimated), .05 = 7.815
RejectH0ifpvalue<.05or2>7.815.
s
TestStatistic
(1) 2 ( 2) 2 (1) 2 (0) 2 ( 1) 2 (1) 2
2 =
+
+
+
+
+
= 1.600
5
5
5
5
5
5
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
43
GoodnessofFitTest:NormalDistribution
s
ConclusionUsingthepValueApproach
AreainUpperTail.90.10.05.025.01
2Value(df=3).5846.2517.8159.34811.345
Because2=1.600isbetween.584and6.251intheChi
SquareDistributionTable,theareaintheuppertail
ofthedistributionisbetween.90and.10.
Thepvalue>.Wecannotrejectthenullhypothesis.
Thereislittleevidencetosupportrejectingtheassumptionthe
populationisnormallydistributedwith=71and=18.54.
Actualpvalueis.6594
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
44
EndofChapter12
2011CengageLearning.AllRightsReserved.Maynotbescanned,copied
orduplicated,orpostedtoapubliclyaccessiblewebsite,inwholeorinpart.
Slide
45
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CHAPTER 9HYPOTHESIS TESTSMULTIPLE CHOICE1. An assumption made about the value of a population parameter is called aa. hypothesisb. conclusionc. confidenced. significanceANS: A2. If the probability of making type II error is not examined, then the
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PRINCIPLES OF MARKETINGSPRING 2012BUAD 3010-002TR 5:45-7:00pm, STN 1200BInstructor:Mr. Donald M. IsenhathPart Time InstructorDepartment of Marketing & SalesOffice:Office hours:Phone:E-mail:Before or after class or by appointment419-530-2420d
Toledo - BUAD - 3010
HOMEWORK ASSIGNMENTS BUAD3010/5410 - SPRING 2012All homework assignments are to be 1-2 pages typed and double spaced.Homework 1 - SWOT Analysis Due 2-2-12Do a SWOT Analysis on yourself. For each area of the analysis, provide at least 4 entries.Expand
Toledo - BUAD - 3010
UnderstandingConsumer and BusinessConsumerBuyer BehaviorChapter5Rest Stop: Previewing theConceptsConcepts1.2.3.4.5.Understand the consumer marketand the major factors that influenceconsumer buyer behavior.Identify and discuss the stages i
Toledo - BUAD - 3010
Customer-DrivenMarketing StrategyMarketingCreating Value forCreatingTarget CustomersTargetChapter6Rest Stop: Previewing theConceptsConcepts1.2.3.4.Define the major steps in designing acustomer-driven marketing strategy:market segmentatio
Toledo - BUAD - 3010
Products, Services,and BrandsandBuilding Customer ValueChapter7Rest Stop: Previewing theConceptsConcepts1.2.3.4.Define product and the majorclassifications of products and services.Describe the decisions companies makeregarding their indiv
Toledo - BUAD - 3010
Developing NewProductsProductsandManaging the Product LifeCycleChapter8Rest Stop: Previewing theConceptsConcepts1.2.3.4.Explain how companies find and developnew-product ideas.List and define the steps in the newproduct development process
Toledo - BUAD - 3010
Marketing Channels:MarketingDelivering Customer ValueChapter10Rest Stop: Previewing theConceptsConcepts1.2.3.4.5.Explain why companies use marketingchannels and discuss the functions thesechannels perform.Discuss how channel members intera
Toledo - BUAD - 3010
CommunicatingCustomer Value:CustomerAdvertising and PublicRelationsChapter12Rest Stop: Previewing theConceptsConcepts1.2.3.4.Define the five promotion mix toolsfor communicating customer value.Discuss the changingcommunications landscape
Toledo - BUAD - 3010
Personal Sellingand Sales PromotionandChapter13Rest Stop: Previewing theConceptsConcepts1.2.3.4.Discuss the role of a companyssalespeople in creating value forcustomers and building customerrelationships.Identify and explain the six major
Toledo - BUAD - 3010
Direct and OnlineMarketing:Marketing:Building Direct CustomerRelationshipsChapter14Rest Stop: Previewing theConceptsConcepts1.2.3.4.5.Define direct marketing and discuss itsbenefits to customers and companies.Identify and discuss the majo
Toledo - BUAD - 3010
The GlobalMarketplaceMarketplaceChapter15Rest Stop: Previewing theConceptsConcepts1.2.3.4.Discuss how the international tradesystem and the economic, politicallegal, and cultural environmentsaffect a companys internationalmarketing decision
Toledo - BUAD - 3010
Marketing:Marketing:Creating andCapturing CustomerValueChapter1Rest Stop: Previewing theConceptsConcepts1.2.3.4.Define marketing and the marketingprocess.Explain the importance of understandingcustomers and identify the five coremarketpl
Toledo - BUAD - 3010
Company andMarketing Strategy:MarketingPartnering to BuildCustomer RelationshipsChapter2Rest Stop: Previewing theConceptsConcepts1.2.3.4.5.Explain companywide strategic planningand its four steps.Discuss how to design business portfolios
Toledo - BUAD - 3010
Analyzing theMarketingEnvironmentEnvironmentChapter3Rest Stop: Previewing theConceptsConcepts1.2.3.4.5.Describe the environmental forces thataffect the companys ability to serve itscustomers.Explain how changes in the demographicand econ
Toledo - BUAD - 3010
Managing MarketingInformationInformationTo Gain Customer InsightsChapter4Rest Stop: Previewing theConceptsConcepts1.2.3.4.5.Explain the importance of informationin gaining insights about themarketplace and customers.Define the marketing i
Toledo - BUAD - 3010
PERSONAL MARKETING PLAN INSTRUCTIONS- BUAD 3010/5410BACKGROUNDComplete one or two paragraphs and no more than one page describing events in your life that shaped youand your behavior. For each item that you mention, you must discuss the impact that the
University of Toronto - ECE - 344
Lecture 1: IntroductionDavid LieECE344University of Toronto1Goals of the Course Understand basic concepts of operating systems Purpose and requirements for an OS Major OS sub-systems Design principles and implementation Build operating systems,
University of Toronto - ECE - 344
Lecture 2: OS FundamentalsDavid LieECE344University of Toronto1Overview Principles in the design of systems software Problems in computer systems Design principles for coping with these problems Reasoning behind an operating systemECE344: Operat
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Lecture 3: The Hardware InterfaceDavid LieECE344University of Toronto1Hardware Resources Hardware resources in a computer system can be broadlyclassified into 3 types: Processor Memory Devices Each have different characteristics and purposes fr
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Lecture 4: OS AbstractionsDavid LieECE344University of Toronto1Overview The fundamental operating system abstractions Threads Virtual memory Bounded buffer Hardware support for operating systems System calls Enforcing protectionECE344: Operat
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Lecture 5: ThreadsDavid LieECE344University of Toronto1OverviewPrograms and thread abstractionThread schedulingThread creation and terminationKernel threads vs. user threadsECE344: Operating Systems2Threads A thread is a stream of instruction
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Lecture 6: ThreadsDavid LieECE344University of Toronto1Overview Thread Implementation Thread scheduling Thread creation and termination Kernel threads vs. user threadsECE344: Operating Systems2Threads and Processes Processes can have multiple
University of Toronto - ECE - 344
Lecture 7: ThreadsDavid LieECE344University of Toronto1Overview Separate OS thread vs Current thread design Kernel threads vs. user threadsECE344: Operating Systems2Does OS have its Own Thread State? Programs have thread state Allows stopping
University of Toronto - ECE - 344
Lecture 8: SynchronizationDavid LieECE344University of Toronto1Overview Need for synchronization Different synchronization primitives Locks Conditional Variables Semaphores Common Synchronization Problems Deadlocks, Livelock, starvation Trade
University of Toronto - ECE - 344
Lecture 9: SynchronizationDavid LieECE344University of Toronto1Overview Need for synchronization Different synchronization primitives Locks Conditional Variables Semaphores Common Synchronization Problems Deadlocks, Livelock, starvation Trade
University of Toronto - ECE - 344
Lecture 10: SynchronizationDavid LieECE344University of Toronto1Overview Need for synchronization Different synchronization primitives Locks Conditional Variables Semaphores Common Synchronization Problems Deadlocks, Livelock, starvation Trad
University of Toronto - ECE - 344
Lecture 11: Unix System Calls and PosixThreadsDavid LieECE344University of Toronto1Overview Process-related Unix system calls Posix threadsECE344: Operating Systems2Process-related Unix System Calls Unix provides process-related system calls f
University of Toronto - ECE - 344
Lecture 12: Memory ManagementDavid LieECE344University of Toronto1Outline Introduction to memory management Fragmentation Paging Hardware Support Virtual Memory Translation Page Tables Linear, Multi-level and inverted Page Tables Memory Resou
University of Toronto - ECE - 344
Lecture 13: Memory ManagementDavid LieECE344University of Toronto1Outline Introduction to memory management Fragmentation Paging Hardware Support Virtual Memory Translation Page Tables Linear, Multi-level and inverted Page Tables Memory Resou
University of Toronto - ECE - 344
Lecture 14: Memory ManagementDavid LieECE344University of Toronto1Outline Introduction to memory management Fragmentation Paging Hardware Support Virtual Memory Translation Page Tables Linear, Multi-level and inverted Page Tables Memory Resou
University of Toronto - ECE - 344
Lecture 15: Memory ManagementDavid LieECE344University of Toronto1Overview The TLB Miss Handler: Page Fault Handler Swap Handler Managing the Swap Area Paging issues and Performance Putting it all together. VM and OS events: Process Creation,
University of Toronto - ECE - 344
Lecture 16: Memory ManagementDavid LieECE344University of Toronto1Overview The TLB Miss Handler: Page Fault Handler Swap Handler Managing the Swap Area Paging issues and Performance Putting it all together. VM and OS events: Process Creation,
University of Toronto - ECE - 344
Lecture 17: Memory ManagementDavid LieECE344University of Toronto1Overview The TLB Miss Handler: Page Fault Handler Swap Handler Managing the Swap Area Paging issues and Performance Putting it all together. VM and OS events: Process Creation,
University of Toronto - ECE - 344
Lecture 18: Page Replacement AlgorithmsDavid LieECE344University of Toronto1OverviewIntroductionPage replacement algorithmsLocal vs. global page replacementPage bufferingThrashingECE344: Operating Systems2Introduction We have seen that OS al
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Lecture 19: Page Replacement AlgorithmsDavid LieECE344University of Toronto1OverviewIntroductionPage replacement algorithmsLocal vs. global page replacementPage bufferingThrashingECE344: Operating Systems2Least Recently Used (LRU) A refineme
University of Toronto - ECE - 344
Lecture 20: SchedulingDavid LieECE344University of Toronto1OverviewPurpose of schedulingScheduling AlgorithmsMultiprocessor IssuesModern SystemsECE344: Operating Systems2Purpose of Scheduling OS scheduler decides when a thread should be run
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Lecture 21: SchedulingDavid LieECE344University of Toronto1OverviewPurpose of schedulingScheduling AlgorithmsMultiprocessor IssuesModern SystemsECE344: Operating Systems2Static Priority Scheduling Each thread is assigned a priority when it is
University of Toronto - ECE - 344
Lecture 22: File SystemsDavid LieECE344University of Toronto1Outline File Systems Overview of file system Disk Basics File system design Consistency and crash recovery Sharing files Unix file system Disks Disk scheduling algorithms Redundan
University of Toronto - ECE - 344
Lecture 23: File SystemsDavid LieECE344University of Toronto1Outline File Systems Overview of file system Disk Basics File system design Consistency and crash recovery Sharing files Unix file system Disks Disk scheduling algorithms Redundan
University of Toronto - ECE - 344
Lecture 24: File SystemsDavid LieECE344University of Toronto1Outline File Systems Overview of file system Disk Basics File system design Consistency and crash recovery Sharing files Unix file system Disks Disk scheduling algorithms Redundan
University of Toronto - ECE - 344
Lecture 25: File SystemsDavid LieECE344University of Toronto1Outline File Systems Overview of file system Disk Basics File system design Consistency and crash recovery Sharing files Unix file system Disks Disk scheduling algorithms Redundan
University of Toronto - ECE - 344
Lecture 26: VirtualizationDavid LieECE344University of Toronto1System Virtualization Operating systems virtualize the CPU and devices so that youcan run multiple, independent processes: Each process is isolated, it believes it has exclusive access
University of Toronto - ECE - 344
Lecture 27: VirtualizationDavid LieECE344University of Toronto1Memory Virtualization Another challenge with VMM implementation is virtualizing theMMU: VMM needs to use MMU to isolate different guest OSs fromeach other Guest OSs need to use MMU t
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Lab 0: OverviewDavid LieECE344University of Toronto1Lab Goals Get familiar with OS161 Learn to build and install a kernel and test environment Get familiar with tools: Cscope: source code navigation Subversion: versioning and collaboration GDB:
University of Toronto - ECE - 344
Lab 1: OverviewDavid LieECE344University of Toronto1Overview Review 3 synchronization types: Locks Semaphores Conditional Variables What is deadlock? Overview of synch.h Tips on DebuggingECE344: Operating Systems2
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Lab 2: OverviewDavid LieECE344University of Toronto1Overview Some notes: How to use splhigh/splx Explanation of system callsECE344: Operating Systems2Splhigh/Splx These disable interrupts Think of this as a global lock for all threads. Everyt
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Lab 2.1: OverviewDavid LieECE344University of Toronto1Function call flowsys_execvsys_forkthread_forkmd_forkentrymd_usermodemips_usermodeECE344: Operating Systems2Md_usermode vs md_forkentrySets up processor for going back to userspace for a
University of Toronto - ECE - 344
Lab 3.0: OverviewDavid LieECE344University of Toronto1Primer Look at kern/arch/mips/include/tlb.h: Description of the TLB interface0xc000000KSEG0 Understand memory layout of MIPS0x8000000 Look at kern/arch/mips/include/vm.h KUSEG: user progra
University of Toronto - ECE - 344
Lab 3.1: OverviewDavid LieECE344University of Toronto1Tasks Major Task: Implement as_copy() so you can support fork() Minor Task Implement sbrk()ECE344: Operating Systems2Implementing as_copy() As_copy() in dumbvm just does a keep copy of the
University of Toronto - ECE - 344
Lab 3.2: OverviewDavid LieECE344University of Toronto1TasksMajor Task: Implement SwappingMinor Task Performance counters and tuningECE344: Operating Systems2Implementing SwapOverview: In your coremap allocation function, you should currently