Test bank For Introduction to Statistical Investigations, 2nd Edition by Nathan Tintle; Beth L. Chance, Complete Chapters 1 - 11, Verified Latest Version

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Introduction to Statistical Investigations,
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2nd Edition Nathan Tintle; Beth L. Chance
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Chapters 1 - 11, Complete
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,TABLE OF CONTENTS
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Chapter 1 – Significance: How Strong is the Evidence
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Chapter 2 – Generalization: How Broadly Do the Results Apply?
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Chapter 3 – Estimation: How Large is the Effect?
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Chapter 4 – Causation: Can We Say What Caused the Effect?
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Chapter 5 – Comparing Two Proportions
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Chapter 6 – Comparing Two Means
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Chapter 7 – Paired Data: One Quantitative Variable
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Chapter 8 – Comparing More Than Two Proportions
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Chapter 9 – Comparing More Than Two Means
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Chapter 10 – Two Quantitative Variables
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Chapter 11 – Modeling Randomness
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,Chapter 1 i




Note: i i iTE i = i Text ientry TE-N i= iText ientry i- iNumericiMa
i = i Matching MS i = i Multiple iselect
MC i = i Multiple ichoice TF i= iTrue-FalseiE i=

i Easy, iM i= iMedium, iH i= iHard


CHAPTER 1 LEARNING OBJECTIVES
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CLO1-1: iUse ithe ichance imodel ito idetermine iwhether ian iobserved istatistic iis iunlikely ito ioccur.
CLO1-2: iCalculate iand iinterpret ia ip-value, iand istate ithe istrength iof ievidence iit iprovides iagainstithe
inull ihypothesis.


CLO1-3: iCalculate ia istandardized istatistic ifor ia isingle iproportion iand ievaluate ithe istrength iof
ievidence iit iprovides iagainst ia inull ihypothesis.


CLO1-4: iDescribe ihow ithe idistance iof ithe iobserved istatistic ifrom ithe iparameter ivalue ispecifiediby ithe
inull ihypothesis, isample i size, iand ione- ivs. itwo-sided i tests iaffect ithe i strength i of ievidence

i against ithe i null ihypothesis.


CLO1-5: iDescribe ihow ito icarry iout ia itheory-based, ione-proportion iz-test.


Section 1.1: Introduction to Chance Models
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LO1.1-1: iRecognize ithe idifference ibetween iparameters iand istatistics.
LO1.1-2: iDescribe ihow ito iuse icoin itossing ito isimulate ioutcomes ifrom ia ichance imodel iof ithe iran-idom
ichoice i between i two i events.


LO1.1-3: iUse ithe iOne iProportion iapplet ito icarry iout ithe icoin itossing isimulation.
LO1.1-4: iIdentify iwhether ior inot istudy iresults iare istatistically isignificant iand iwhether ior inot ithe
ichance i model iis ia i plausible iexplanation ifor ithe idata.


LO1.1-5: iImplement ithe i3S istrategy: ifind ia istatistic, isimulate iresults ifrom ia ichance imodel, iand
icomment ion istrength iof ievidence iagainst iobserved istudy iresults ihappening iby ichance

ialone.


LO1.1-6: iDifferentiate ibetween isaying ithe ichance imodel iis iplausible iand ithe ichance imodel iis ithe
icorrect iexplanation ifor ithe iobserved idata.




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, 1-2 Test iBank ifor iIntroduction ito iStatistical iInvestigations, i2nd iEdition


Questions i1 ithrough i4:
Do ired iuniform iwearers itend ito iwin imore ioften ithan ithose iwearing iblue iuniforms iin
iTaekwondo imatches iwhere icompetitors iare irandomly iassigned ito iwear ieither ia ired ior iblue

iuniform? iIn ia isample iof i80 iTaekwondo imatches, ithere iwere i45 imatches iwhere itheired

iuniform iwearer iwon.


1. What iis ithe iparameter iof iinterest ifor ithis istudy?
A. The ilong-run iproportion iof iTaekwondo imatches iin iwhich ithe ired iuniform iwearer
iwins


B. The iproportion iof imatches iin iwhich ithe ired iuniform iwearer iwins iin ia isample iof i80
iTaekwondo i matches


C. Whether ithe ired iuniform iwearer iwins ia imatch
D. i 0.50
Ans: iA; iLO: i1.1-1; iDifficulty: iEasy; iType: iMC
2. What iis ithe istatistic ifor ithis istudy?
A. The ilong-run iproportion iof iTaekwondo imatches iin iwhich ithe ired iuniform iwearer
iwins


B. The iproportion iof imatches iin iwhich ithe ired iuniform iwearer iwins iin ia isample iof i80
iTaekwondo i matches


C. Whether ithe ired iuniform iwearer iwins ia imatch
D. i 0.50
Ans: iB; iLO: i1.1-1; iDifficulty: iEasy; iType: iMC
3. Given ibelow iis ithe isimulated idistribution iof ithe inumber iof i―red iwins‖ ithat icould ihappen iby
ichance ialone iin ia isample iof i80 imatches. iBased ion ithis isimulation, iis iour iobserved iresult

istatistically isignificant?




A. Yes, isince i45 iis ilarger ithan i40.
B. Yes, isince ithe iheight iof ithe idotplot iabove i45 iis ismaller ithan ithe iheight iof ithe
idotplot iabove i40.


C. No, isince i45 iis ia ifairly itypical ioutcome iif ithe icolor iof ithe iwinner‘s iuniform iwas
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