TABLE OF CONTENTS n n n
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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FOR INSTRUCTOR USE ONLY
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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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FOR INSTRUCTOR USE ONLY
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,Chapter 1 n
Note: TE = Text entry
nnn n n n TE-N = Text entry - NumericMa
n n n n n n
n = Matching n MS = Multiple select n n n
MC = Multiple choice n n n TF = True-FalseE =
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n Easy, M = Medium, H = Hard n n n n n n
CHAPTER 1 LEARNING OBJECTIVES n n n
CLO1-1: Use the chance model to determine whether an observed statistic is unlikely to occur.
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CLO1-2: Calculate and interpret a p-value, and state the strength of evidence it provides againstthe null
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hypothesis.
n
CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength ofevidence
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it provides against a null hypothesis.
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CLO1-4: Describe how the distance of the observed statistic from the parameter value specifiedby the null
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hypothesis, sample size, and one- vs. two-sided tests affect the strength of evidence against the
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null hypothesis.
n n
CLO1-5: Describe how to carry out a theory-based, one-proportion z-test.
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Section 1.1: Introduction to Chance Models n n n n n
LO1.1-1: Recognize the difference between parameters and statistics.
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LO1.1-2: Describe how to use coin tossing to simulate outcomes from a chance model of the ran-dom
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choice between two events.
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LO1.1-3: Use the One Proportion applet to carry out the coin tossing simulation.
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LO1.1-4: Identify whether or not study results are statistically significant and whether or not thechance
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model is a plausible explanation for the data.
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LO1.1-5: Implement the 3S strategy: find a statistic, simulate results from a chance model,
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and comment on strength of evidence against observed study results happening by chance
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alone. n
LO1.1-6: Differentiate between saying the chance model is plausible and the chance model is the correct
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explanation for the observed data.
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FOR INSTRUCTOR USE ONLY n n n
, 1-2 Test Bank for Introduction to Statistical Investigations, 2nd Edition
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Questions 1 through 4:
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Do red uniform wearers tend to win more often than those wearing blue uniforms in Taekwondo
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matches where competitors are randomly assigned to wear either a red or blue uniform? In a
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sample of 80 Taekwondo matches, there were 45 matches where thered uniform wearer won.
n n n n n n n n n n n n n n n
1. What is the parameter of interest for this study?
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A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
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B. The proportion of matches in which the red uniform wearer wins in a sample of
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80Taekwondo matches
n n n
C. Whether the red uniform wearer wins a match n n n n n n n
D. 0.50 n
Ans: A; LO: 1.1-1; Difficulty: Easy; Type: MC
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2. What is the statistic for this study?
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A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
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B. The proportion of matches in which the red uniform wearer wins in a sample of
n n n n n n n n n n n n n n
80Taekwondo matches
n n n
C. Whether the red uniform wearer wins a match n n n n n n n
D. 0.50 n
Ans: B; LO: 1.1-1; Difficulty: Easy; Type: MC
n n n n n n n
3. Given below is the simulated distribution of the number of ―red wins‖ that could happen
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by chance alone in a sample of 80 matches. Based on this simulation, is our observed result
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statistically significant?
n n
A. Yes, since 45 is larger than 40. n n n n n n
B. Yes, since the height of the dotplot above 45 is smaller than the height of
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thedotplot above 40.
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C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform
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wasdetermined by chance alone.
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FOR INSTRUCTOR USE ONLY n n n
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?
n n n n n n n n n
Chapter 4 – Causation: Can We Say What Caused the Effect?
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FOR INSTRUCTOR USE ONLY
n n n
,Chapter 5 – Comparing Two Proportions
n n n n n n
Chapter 6 – Comparing Two Means
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Chapter 7 – Paired Data: One Quantitative Variable
n n n n n n n n
Chapter 8 – Comparing More Than Two Proportions
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Chapter 9 – Comparing More Than Two Means
n n n n n n n n
Chapter 10 – Two Quantitative Variables
n n n n n n
Chapter 11 – Modeling Randomness
n n n n
FOR INSTRUCTOR USE ONLY
n n n
,Chapter 1 n
Note: TE = Text entry
nnn n n n TE-N = Text entry - NumericMa
n n n n n n
n = Matching n MS = Multiple select n n n
MC = Multiple choice n n n TF = True-FalseE =
n n n n
n Easy, M = Medium, H = Hard n n n n n n
CHAPTER 1 LEARNING OBJECTIVES n n n
CLO1-1: Use the chance model to determine whether an observed statistic is unlikely to occur.
n n n n n n n n n n n n n n
CLO1-2: Calculate and interpret a p-value, and state the strength of evidence it provides againstthe null
n n n n n n n n n n n n n n n n
hypothesis.
n
CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength ofevidence
n n n n n n n n n n n n n n
it provides against a null hypothesis.
n n n n n n
CLO1-4: Describe how the distance of the observed statistic from the parameter value specifiedby the null
n n n n n n n n n n n n n n n n
hypothesis, sample size, and one- vs. two-sided tests affect the strength of evidence against the
n n n n n n n n n n n n n n n
null hypothesis.
n n
CLO1-5: Describe how to carry out a theory-based, one-proportion z-test.
n n n n n n n n n
Section 1.1: Introduction to Chance Models n n n n n
LO1.1-1: Recognize the difference between parameters and statistics.
n n n n n n n
LO1.1-2: Describe how to use coin tossing to simulate outcomes from a chance model of the ran-dom
n n n n n n n n n n n n n n n n n
choice between two events.
n n n n
LO1.1-3: Use the One Proportion applet to carry out the coin tossing simulation.
n n n n n n n n n n n n
LO1.1-4: Identify whether or not study results are statistically significant and whether or not thechance
n n n n n n n n n n n n n n n
model is a plausible explanation for the data.
n n n n n n n n
LO1.1-5: Implement the 3S strategy: find a statistic, simulate results from a chance model,
n n n n n n n n n n n n n
and comment on strength of evidence against observed study results happening by chance
n n n n n n n n n n n n n
alone. n
LO1.1-6: Differentiate between saying the chance model is plausible and the chance model is the correct
n n n n n n n n n n n n n n n
explanation for the observed data.
n n n n n
FOR INSTRUCTOR USE ONLY n n n
, 1-2 Test Bank for Introduction to Statistical Investigations, 2nd Edition
n n n n n n n n
Questions 1 through 4:
n n n
Do red uniform wearers tend to win more often than those wearing blue uniforms in Taekwondo
n n n n n n n n n n n n n n n
matches where competitors are randomly assigned to wear either a red or blue uniform? In a
n n n n n n n n n n n n n n n n
sample of 80 Taekwondo matches, there were 45 matches where thered uniform wearer won.
n n n n n n n n n n n n n n n
1. What is the parameter of interest for this study?
n n n n n n n n
A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
n n n n n n n n n n n n
B. The proportion of matches in which the red uniform wearer wins in a sample of
n n n n n n n n n n n n n n
80Taekwondo matches
n n n
C. Whether the red uniform wearer wins a match n n n n n n n
D. 0.50 n
Ans: A; LO: 1.1-1; Difficulty: Easy; Type: MC
n n n n n n n
2. What is the statistic for this study?
n n n n n n
A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
n n n n n n n n n n n n
B. The proportion of matches in which the red uniform wearer wins in a sample of
n n n n n n n n n n n n n n
80Taekwondo matches
n n n
C. Whether the red uniform wearer wins a match n n n n n n n
D. 0.50 n
Ans: B; LO: 1.1-1; Difficulty: Easy; Type: MC
n n n n n n n
3. Given below is the simulated distribution of the number of ―red wins‖ that could happen
n n n n n n n n n n n n n n
by chance alone in a sample of 80 matches. Based on this simulation, is our observed result
n n n n n n n n n n n n n n n n n
statistically significant?
n n
A. Yes, since 45 is larger than 40. n n n n n n
B. Yes, since the height of the dotplot above 45 is smaller than the height of
n n n n n n n n n n n n n n
thedotplot above 40.
n n n n
C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform
n n n n n n n n n n n n n n
wasdetermined by chance alone.
n n n n n
FOR INSTRUCTOR USE ONLY n n n
