TEST BANK
Introduction to Statistical Investigations,
nd
2 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 red uniform wearers tend to win more often than those wearing blue uniforms in
Taekwondo matches where competitors are randomly assigned to wear either a red or blue
uniform? In a sample of 80 Taekwondo matches, there were 45 matches where thered uniform
wearer won.
1. What is the parameter of interest for this study?
A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
B. The proportion of matches in which the red uniform wearer wins in a sample of 80
Taekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: A; LO: 1.1-1; Difficulty: Easy; Type: MC
2. What is the statistic for this study?
A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
B. The proportion of matches in which the red uniform wearer wins in a sample of 80
Taekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: B; LO: 1.1-1; Difficulty: Easy; Type: MC
3. Given below is the simulated distribution of the number of ―red wins‖ that could happen by
chance alone in a sample of 80 matches. Based on this simulation, is our observed result
statistically significant?
A. Yes, since 45 is larger than 40.
B. Yes, since the height of the dotplot above 45 is smaller than the height of the
dotplot above 40.
C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform was
determined by chance alone.
FOR INSTRUCTOR USE ONLY
Introduction to Statistical Investigations,
nd
2 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 red uniform wearers tend to win more often than those wearing blue uniforms in
Taekwondo matches where competitors are randomly assigned to wear either a red or blue
uniform? In a sample of 80 Taekwondo matches, there were 45 matches where thered uniform
wearer won.
1. What is the parameter of interest for this study?
A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
B. The proportion of matches in which the red uniform wearer wins in a sample of 80
Taekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: A; LO: 1.1-1; Difficulty: Easy; Type: MC
2. What is the statistic for this study?
A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
B. The proportion of matches in which the red uniform wearer wins in a sample of 80
Taekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: B; LO: 1.1-1; Difficulty: Easy; Type: MC
3. Given below is the simulated distribution of the number of ―red wins‖ that could happen by
chance alone in a sample of 80 matches. Based on this simulation, is our observed result
statistically significant?
A. Yes, since 45 is larger than 40.
B. Yes, since the height of the dotplot above 45 is smaller than the height of the
dotplot above 40.
C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform was
determined by chance alone.
FOR INSTRUCTOR USE ONLY
