Solutions Manual forM M
Design and Analysis
M M M
Mof Experiments, 10e
M M
M Douglas Montgomery
M
(All Chapters)
M M
, Solutions Mfrom MMontgomery, MD. MC. M(2019) MDesign Mand MAnalysis Mof MExperiments,
MWiley, MNY
Chapter 1 M
Introductio
M
n Solutions
M
1.1S. M Suppose Mthat Myou Mwant Mto Mdesign Man Mexperiment Mto Mstudy Mthe Mproportion Mof Munpopped
Mkernels Mof Mpopcorn. M Complete Msteps M1-3 Mof Mthe Mguidelines Mfor Mdesigning Mexperiments Min MSection
M1.4. M Are Mthere Many Mmajor Msources Mof Mvariation Mthat Mwould Mbe Mdifficult Mto Mcontrol?
Step M1 M– MRecognition Mof Mand Mstatement Mof Mthe Mproblem. M Possible Mproblem Mstatement Mwould
Mbe M– Mfind Mthe Mbest Mcombination Mof Minputs Mthat Mmaximizes Myield Mon Mpopcorn M– Mminimize
Munpopped Mkernels.
Step M2 M– MSelection Mof Mthe Mresponse Mvariable. M Possible Mresponses Mare Mnumber Mof Munpopped
Mkernels Mper M100 Mkernals Min Mexperiment, Mweight Mof Munpopped Mkernels Mversus Mthe Mtotal Mweight
Mof Mkernels Mcooked.
Step M3 M– MChoice Mof Mfactors, Mlevels Mand Mrange. M Possible Mfactors Mand Mlevels Mare Mbrand Mof
Mpopcorn M(levels: Mcheap, Mexpensive), Mage Mof Mpopcorn M(levels: Mfresh, Mold), Mtype Mof Mcooking
Mmethod M(levels: Mstovetop, Mmicrowave), Mtemperature M(levels: M150C, M250C), Mcooking Mtime M(levels: M3
Mminutes, M5 Mminutes), Mamount Mof Mcooking Moil M(levels, M1 Moz, M3 Moz), Metc.
1.2. Suppose Mthat Myou Mwant Mto Minvestigate Mthe Mfactors Mthat Mpotentially Maffect Mcooked Mrice.
(a) What Mwould Myou Muse Mas Ma Mresponse Mvariable Min Mthis Mexperiment? M How Mwould
Myou Mmeasure Mthe Mresponse?
(b) List Mall Mof Mthe Mpotential Msources Mof Mvariability Mthat Mcould Mimpact Mthe Mresponse.
(c) Complete Mthe Mfirst Mthree Msteps Mof Mthe Mguidelines Mfor Mdesigning Mexperiments Min MSection M1.4.
Step M1 M– MRecognition Mof Mand Mstatement Mof Mthe
Mproblem. MStep M2 M– MSelection Mof Mthe Mresponse
Mvariable.
Step M3 M– MChoice Mof Mfactors, Mlevels Mand Mrange.
1.3. Suppose Mthat Myou Mwant Mto Mcompare Mthe Mgrowth Mof Mgarden Mflowers Mwith Mdifferent
Mconditions Mof Msunlight, Mwater, Mfertilizer Mand Msoil Mconditions. M Complete Msteps M1-3 Mof
Mthe Mguidelines Mfor Mdesigning Mexperiments Min MSection M1.4.
Step M1 M– MRecognition Mof Mand Mstatement Mof Mthe
Mproblem. MStep M2 M– MSelection Mof Mthe Mresponse
Mvariable.
Step M3 M– MChoice Mof Mfactors, Mlevels Mand Mrange.
1-1
, Solutions Mfrom MMontgomery, MD. MC. M(2019) MDesign Mand MAnalysis Mof MExperiments,
1.4. Select Man
MWiley, Mexperiment Mof Minterest Mto Myou. M Complete Msteps M1-3 Mof Mthe Mguidelines
MNY
Mfor Mdesigning Mexperiments Min MSection M1.4.
1-2
, Solutions Mfrom MMontgomery, MD. MC. M(2019) MDesign Mand MAnalysis Mof MExperiments,
MWiley, MNY
1.5. Search Mthe MWorld MWide MWeb Mfor Minformation Mabout MSir MRonald MA. MFisher Mand
Mhis Mwork Mon Mexperimental Mdesign Min Magricultural Mscience Mat Mthe MRothamsted
MExperimental MStation.
Sample Msearches Mcould Minclude Mthe Mfollowing:
1.6. Find Ma MWeb MSite Mfor Ma Mbusiness Mthat Myou Mare Minterested Min. M Develop Ma Mlist Mof
Mfactors Mthat Myou Mwould Muse Min Man Mexperimental Mdesign Mto Mimprove Mthe Meffectiveness Mof
Mthis MWeb MSite.
1.7. Almost Meveryone Mis Mconcerned Mabout Mthe Mrising Mprice Mof Mgasoline. M Construct Ma Mcause
Mand Meffect Mdiagram Midentifying Mthe Mfactors Mthat Mpotentially Minfluence Mthe Mgasoline Mmileage
Mthat Myou Mget Min Myour Mcar. MHow Mwould Myou Mgo Mabout Mconducting Man Mexperiment Mto
Mdetermine Many Mof Mthese Mfactors Mactually Maffect Myour Mgasoline Mmileage?
1.8. What Mis Mreplication? M Why Mdo Mwe Mneed Mreplication Min Man Mexperiment? M Present
Man Mexample Mthat Millustrates Mthe Mdifferences Mbetween Mreplication Mand Mrepeated Mmeasures.
Repetition Mof Mthe Mexperimental Mruns. M Replication Menables Mthe Mexperimenter Mto Mestimate Mthe
Mexperimental Merror, Mand Mprovides Mmore Mprecise Mestimate Mof Mthe Mmean Mfor Mthe Mresponse
Mvariable.
1.9 S. M Why Mis Mrandomization Mimportant Min Man Mexperiment?
To Massure Mthe Mobservations, Mor Merrors, Mare Mindependently Mdistributed Mrandome Mvariables Mas
Mrequired Mby Mstatistical Mmethods. M Also, Mto M“average Mout” Mthe Meffects Mof Mextraneous Mfactors Mthat
Mmight Moccur Mwhile Mrunning Mthe Mexperiment.
1.10 S. MWhat Mare Mthe Mpotential Mrisks Mof Ma Msingle, Mlarge, Mcomprehensive Mexperiment Min Mcontrast
Mto Ma Msequential Mapproach?
The Mimportant Mfactors Mand Mlevels Mare Mnot Malways Mknown Mat Mthe Mbeginning Mof Mthe Mexperimental
Mprocess. M Even Mnew Mresponse Mvariables Mmight Mbe Mdiscovered Mduring Mthe Mexperimental Mprocess.
M By Mrunning Ma Mlarge Mcomprehensive Mexperiment, Mvaluable Minformation Mlearned Mearly Min Mthe
Mexperimental Mprocess Mcan Mnot Mlikely Mbe Mincorporated Min Mthe Mremaining Mexperimental Mruns.
1-3
Design and Analysis
M M M
Mof Experiments, 10e
M M
M Douglas Montgomery
M
(All Chapters)
M M
, Solutions Mfrom MMontgomery, MD. MC. M(2019) MDesign Mand MAnalysis Mof MExperiments,
MWiley, MNY
Chapter 1 M
Introductio
M
n Solutions
M
1.1S. M Suppose Mthat Myou Mwant Mto Mdesign Man Mexperiment Mto Mstudy Mthe Mproportion Mof Munpopped
Mkernels Mof Mpopcorn. M Complete Msteps M1-3 Mof Mthe Mguidelines Mfor Mdesigning Mexperiments Min MSection
M1.4. M Are Mthere Many Mmajor Msources Mof Mvariation Mthat Mwould Mbe Mdifficult Mto Mcontrol?
Step M1 M– MRecognition Mof Mand Mstatement Mof Mthe Mproblem. M Possible Mproblem Mstatement Mwould
Mbe M– Mfind Mthe Mbest Mcombination Mof Minputs Mthat Mmaximizes Myield Mon Mpopcorn M– Mminimize
Munpopped Mkernels.
Step M2 M– MSelection Mof Mthe Mresponse Mvariable. M Possible Mresponses Mare Mnumber Mof Munpopped
Mkernels Mper M100 Mkernals Min Mexperiment, Mweight Mof Munpopped Mkernels Mversus Mthe Mtotal Mweight
Mof Mkernels Mcooked.
Step M3 M– MChoice Mof Mfactors, Mlevels Mand Mrange. M Possible Mfactors Mand Mlevels Mare Mbrand Mof
Mpopcorn M(levels: Mcheap, Mexpensive), Mage Mof Mpopcorn M(levels: Mfresh, Mold), Mtype Mof Mcooking
Mmethod M(levels: Mstovetop, Mmicrowave), Mtemperature M(levels: M150C, M250C), Mcooking Mtime M(levels: M3
Mminutes, M5 Mminutes), Mamount Mof Mcooking Moil M(levels, M1 Moz, M3 Moz), Metc.
1.2. Suppose Mthat Myou Mwant Mto Minvestigate Mthe Mfactors Mthat Mpotentially Maffect Mcooked Mrice.
(a) What Mwould Myou Muse Mas Ma Mresponse Mvariable Min Mthis Mexperiment? M How Mwould
Myou Mmeasure Mthe Mresponse?
(b) List Mall Mof Mthe Mpotential Msources Mof Mvariability Mthat Mcould Mimpact Mthe Mresponse.
(c) Complete Mthe Mfirst Mthree Msteps Mof Mthe Mguidelines Mfor Mdesigning Mexperiments Min MSection M1.4.
Step M1 M– MRecognition Mof Mand Mstatement Mof Mthe
Mproblem. MStep M2 M– MSelection Mof Mthe Mresponse
Mvariable.
Step M3 M– MChoice Mof Mfactors, Mlevels Mand Mrange.
1.3. Suppose Mthat Myou Mwant Mto Mcompare Mthe Mgrowth Mof Mgarden Mflowers Mwith Mdifferent
Mconditions Mof Msunlight, Mwater, Mfertilizer Mand Msoil Mconditions. M Complete Msteps M1-3 Mof
Mthe Mguidelines Mfor Mdesigning Mexperiments Min MSection M1.4.
Step M1 M– MRecognition Mof Mand Mstatement Mof Mthe
Mproblem. MStep M2 M– MSelection Mof Mthe Mresponse
Mvariable.
Step M3 M– MChoice Mof Mfactors, Mlevels Mand Mrange.
1-1
, Solutions Mfrom MMontgomery, MD. MC. M(2019) MDesign Mand MAnalysis Mof MExperiments,
1.4. Select Man
MWiley, Mexperiment Mof Minterest Mto Myou. M Complete Msteps M1-3 Mof Mthe Mguidelines
MNY
Mfor Mdesigning Mexperiments Min MSection M1.4.
1-2
, Solutions Mfrom MMontgomery, MD. MC. M(2019) MDesign Mand MAnalysis Mof MExperiments,
MWiley, MNY
1.5. Search Mthe MWorld MWide MWeb Mfor Minformation Mabout MSir MRonald MA. MFisher Mand
Mhis Mwork Mon Mexperimental Mdesign Min Magricultural Mscience Mat Mthe MRothamsted
MExperimental MStation.
Sample Msearches Mcould Minclude Mthe Mfollowing:
1.6. Find Ma MWeb MSite Mfor Ma Mbusiness Mthat Myou Mare Minterested Min. M Develop Ma Mlist Mof
Mfactors Mthat Myou Mwould Muse Min Man Mexperimental Mdesign Mto Mimprove Mthe Meffectiveness Mof
Mthis MWeb MSite.
1.7. Almost Meveryone Mis Mconcerned Mabout Mthe Mrising Mprice Mof Mgasoline. M Construct Ma Mcause
Mand Meffect Mdiagram Midentifying Mthe Mfactors Mthat Mpotentially Minfluence Mthe Mgasoline Mmileage
Mthat Myou Mget Min Myour Mcar. MHow Mwould Myou Mgo Mabout Mconducting Man Mexperiment Mto
Mdetermine Many Mof Mthese Mfactors Mactually Maffect Myour Mgasoline Mmileage?
1.8. What Mis Mreplication? M Why Mdo Mwe Mneed Mreplication Min Man Mexperiment? M Present
Man Mexample Mthat Millustrates Mthe Mdifferences Mbetween Mreplication Mand Mrepeated Mmeasures.
Repetition Mof Mthe Mexperimental Mruns. M Replication Menables Mthe Mexperimenter Mto Mestimate Mthe
Mexperimental Merror, Mand Mprovides Mmore Mprecise Mestimate Mof Mthe Mmean Mfor Mthe Mresponse
Mvariable.
1.9 S. M Why Mis Mrandomization Mimportant Min Man Mexperiment?
To Massure Mthe Mobservations, Mor Merrors, Mare Mindependently Mdistributed Mrandome Mvariables Mas
Mrequired Mby Mstatistical Mmethods. M Also, Mto M“average Mout” Mthe Meffects Mof Mextraneous Mfactors Mthat
Mmight Moccur Mwhile Mrunning Mthe Mexperiment.
1.10 S. MWhat Mare Mthe Mpotential Mrisks Mof Ma Msingle, Mlarge, Mcomprehensive Mexperiment Min Mcontrast
Mto Ma Msequential Mapproach?
The Mimportant Mfactors Mand Mlevels Mare Mnot Malways Mknown Mat Mthe Mbeginning Mof Mthe Mexperimental
Mprocess. M Even Mnew Mresponse Mvariables Mmight Mbe Mdiscovered Mduring Mthe Mexperimental Mprocess.
M By Mrunning Ma Mlarge Mcomprehensive Mexperiment, Mvaluable Minformation Mlearned Mearly Min Mthe
Mexperimental Mprocess Mcan Mnot Mlikely Mbe Mincorporated Min Mthe Mremaining Mexperimental Mruns.
1-3
