TEST BANK
Introduction to Statistical Investigations,
2nd Edition Nathan Tintle; Beth L. Chance
Chapters 1 - 11, Complete
FOR INSTRUCTOR USE ONLY
,TABLE OF CONTENTS
Chapter 1 – Significance: How Strong is the Evidence
Chapter 2 – Generalization: How Broadly Do the Results Apply?
Chapter 3 – Estimation: How Large is the Effect?
Chapter 4 – Causation: Can We Say What Caused the Effect?
Chapter 5 – Comparing Two Proportions
Chapter 6 – Comparing Two Means
Chapter 7 – Paired Data: One Quantitative Variable
Chapter 8 – Comparing More Than Two Proportions
Chapter 9 – Comparing More Than Two Means
Chapter 10 – Two Quantitative Variables
Chapter 11 – Modeling Randomness
FOR INSTRUCTOR USE ONLY
,Chapter 1
Note: TE = Text entry TE-N = Text entry - NumericMa
= Matching MS = Multiple select
MC = Multiple choice TF = True-FalseE =
Easy, M = Medium, H = Hard
CHAPTER 1 LEARNING OBJECTIVES
CLO1-1: Use the chance model to determine whether an observed statistic is unlikely to occur.
CLO1-2: Calculate and interpret a p-value, and state the strength of evidence it provides againstthe null
hypothesis.
CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength of
evidence it provides against a null hypothesis.
CLO1-4: Describe how the distance of the observed statistic from the parameter value specifiedby the
null hypothesis, sample size, and one- vs. two-sided tests affect the strength of evidence against
the null hypothesis.
CLO1-5: Describe how to carry out a theory-based, one-proportion z-test.
Section 1.1: Introduction to Chance Models
LO1.1-1: Recognize the difference between parameters and statistics.
LO1.1-2: Describe how to use coin tossing to simulate outcomes from a chance model of the ran-dom
choice between two events.
LO1.1-3: Use the One Proportion applet to carry out the coin tossing simulation.
LO1.1-4: Identify whether or not study results are statistically significant and whether or not the
chance model is a plausible explanation for the data.
LO1.1-5: Implement the 3S strategy: find a statistic, simulate results from a chance model, and
comment on strength of evidence against observed study results happening by chance alone.
LO1.1-6: Differentiate between saying the chance model is plausible and the chance model is the correct
explanation for the observed data.
FOR INSTRUCTOR USE ONLY
, 1-2 TestkBankkforkIntroductionktokStatisticalkInvestigations,k2ndkEdition
Questionsk1kthroughk4:
DokredkuniformkwearersktendktokwinkmorekoftenkthankthosekwearingkbluekuniformskinkTaekwondo
kmatcheskwherekcompetitorskarekrandomlykassignedktokwearkeitherkakredkorkbluekuniform?kInkaksa
mplekofk80kTaekwondokmatches,ktherekwerek45kmatcheskwherekthekredkuniformkwearerkwon.
1. Whatkiskthekparameterkofkinterestkforkthiskstudy?
A. Theklong-runkproportionkofkTaekwondokmatcheskinkwhichkthekredkuniformkwearerkwins
B. Thekproportionkofkmatcheskinkwhichkthekredkuniformkwearerkwinskinkaksamplekofk80kTaekw
ondok matches
C. Whetherkthekredkuniformkwearerkwinskakmatch
D.k 0.50
Ans:kA;kLO:k1.1-1;kDifficulty:kEasy;kType:kMC
2. Whatkiskthekstatistickforkthiskstudy?
A. Theklong-runkproportionkofkTaekwondokmatcheskinkwhichkthekredkuniformkwearerkwins
B. Thekproportionkofkmatcheskinkwhichkthekredkuniformkwearerkwinskinkaksamplekofk80kTaekw
ondok matches
C. Whetherkthekredkuniformkwearerkwinskakmatch
D.k 0.50
Ans:kB;kLO:k1.1-1;kDifficulty:kEasy;kType:kMC
3. Givenkbelowkisktheksimulatedkdistributionkofktheknumberkofk―redkwins‖kthatkcouldkhappenkbykchanc
ekalonekinkaksamplekofk80kmatches.kBasedkonkthisksimulation,kiskourkobservedkresultkstatisticallyksig
nificant?
A. Yes,ksincek45kisklargerkthank40.
B. Yes,ksincekthekheightkofkthekdotplotkabovek45kisksmallerkthankthekheightkofkthekdotplo
tkabovek40.
C. No,ksincek45kiskakfairlyktypicalkoutcomekifkthekcolorkofkthekwinner‘skuniformkwaskdeter
minedkbykchancekalone.
FOR INSTRUCTOR USE ONLY
Introduction to Statistical Investigations,
2nd Edition Nathan Tintle; Beth L. Chance
Chapters 1 - 11, Complete
FOR INSTRUCTOR USE ONLY
,TABLE OF CONTENTS
Chapter 1 – Significance: How Strong is the Evidence
Chapter 2 – Generalization: How Broadly Do the Results Apply?
Chapter 3 – Estimation: How Large is the Effect?
Chapter 4 – Causation: Can We Say What Caused the Effect?
Chapter 5 – Comparing Two Proportions
Chapter 6 – Comparing Two Means
Chapter 7 – Paired Data: One Quantitative Variable
Chapter 8 – Comparing More Than Two Proportions
Chapter 9 – Comparing More Than Two Means
Chapter 10 – Two Quantitative Variables
Chapter 11 – Modeling Randomness
FOR INSTRUCTOR USE ONLY
,Chapter 1
Note: TE = Text entry TE-N = Text entry - NumericMa
= Matching MS = Multiple select
MC = Multiple choice TF = True-FalseE =
Easy, M = Medium, H = Hard
CHAPTER 1 LEARNING OBJECTIVES
CLO1-1: Use the chance model to determine whether an observed statistic is unlikely to occur.
CLO1-2: Calculate and interpret a p-value, and state the strength of evidence it provides againstthe null
hypothesis.
CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength of
evidence it provides against a null hypothesis.
CLO1-4: Describe how the distance of the observed statistic from the parameter value specifiedby the
null hypothesis, sample size, and one- vs. two-sided tests affect the strength of evidence against
the null hypothesis.
CLO1-5: Describe how to carry out a theory-based, one-proportion z-test.
Section 1.1: Introduction to Chance Models
LO1.1-1: Recognize the difference between parameters and statistics.
LO1.1-2: Describe how to use coin tossing to simulate outcomes from a chance model of the ran-dom
choice between two events.
LO1.1-3: Use the One Proportion applet to carry out the coin tossing simulation.
LO1.1-4: Identify whether or not study results are statistically significant and whether or not the
chance model is a plausible explanation for the data.
LO1.1-5: Implement the 3S strategy: find a statistic, simulate results from a chance model, and
comment on strength of evidence against observed study results happening by chance alone.
LO1.1-6: Differentiate between saying the chance model is plausible and the chance model is the correct
explanation for the observed data.
FOR INSTRUCTOR USE ONLY
, 1-2 TestkBankkforkIntroductionktokStatisticalkInvestigations,k2ndkEdition
Questionsk1kthroughk4:
DokredkuniformkwearersktendktokwinkmorekoftenkthankthosekwearingkbluekuniformskinkTaekwondo
kmatcheskwherekcompetitorskarekrandomlykassignedktokwearkeitherkakredkorkbluekuniform?kInkaksa
mplekofk80kTaekwondokmatches,ktherekwerek45kmatcheskwherekthekredkuniformkwearerkwon.
1. Whatkiskthekparameterkofkinterestkforkthiskstudy?
A. Theklong-runkproportionkofkTaekwondokmatcheskinkwhichkthekredkuniformkwearerkwins
B. Thekproportionkofkmatcheskinkwhichkthekredkuniformkwearerkwinskinkaksamplekofk80kTaekw
ondok matches
C. Whetherkthekredkuniformkwearerkwinskakmatch
D.k 0.50
Ans:kA;kLO:k1.1-1;kDifficulty:kEasy;kType:kMC
2. Whatkiskthekstatistickforkthiskstudy?
A. Theklong-runkproportionkofkTaekwondokmatcheskinkwhichkthekredkuniformkwearerkwins
B. Thekproportionkofkmatcheskinkwhichkthekredkuniformkwearerkwinskinkaksamplekofk80kTaekw
ondok matches
C. Whetherkthekredkuniformkwearerkwinskakmatch
D.k 0.50
Ans:kB;kLO:k1.1-1;kDifficulty:kEasy;kType:kMC
3. Givenkbelowkisktheksimulatedkdistributionkofktheknumberkofk―redkwins‖kthatkcouldkhappenkbykchanc
ekalonekinkaksamplekofk80kmatches.kBasedkonkthisksimulation,kiskourkobservedkresultkstatisticallyksig
nificant?
A. Yes,ksincek45kisklargerkthank40.
B. Yes,ksincekthekheightkofkthekdotplotkabovek45kisksmallerkthankthekheightkofkthekdotplo
tkabovek40.
C. No,ksincek45kiskakfairlyktypicalkoutcomekifkthekcolorkofkthekwinner‘skuniformkwaskdeter
minedkbykchancekalone.
FOR INSTRUCTOR USE ONLY
