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 Test Bank for Introduction to Statistical Investigations, 2nd Edition
Questions 1 through 4:
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
iweareriwins
B. The iproportion iof imatches iin iwhich ithe ired iuniform iwearer iwins iin ia isample iof
i80iTaekwondo imatches
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
iweareriwins
B. The iproportion iof imatches iin iwhich ithe ired iuniform iwearer iwins iin ia isample iof
i80iTaekwondo imatches
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
itheidotplot iabove i40.
C. No, isince i45 iis ia ifairly itypical ioutcome iif ithe icolor iof ithe iwinner‘s iuniform
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 Test Bank for Introduction to Statistical Investigations, 2nd Edition
Questions 1 through 4:
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
iweareriwins
B. The iproportion iof imatches iin iwhich ithe ired iuniform iwearer iwins iin ia isample iof
i80iTaekwondo imatches
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
iweareriwins
B. The iproportion iof imatches iin iwhich ithe ired iuniform iwearer iwins iin ia isample iof
i80iTaekwondo imatches
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
itheidotplot iabove i40.
C. No, isince i45 iis ia ifairly itypical ioutcome iif ithe icolor iof ithe iwinner‘s iuniform
FOR INSTRUCTOR USE ONLY
