TEST BANK
Introduction to Statistical Investigations,
2nd Edition Nathan Tintle; Beth L. Chance
Chapters - 1, Complete
W W
FOR INSTRUCTOR USE ONL
,TABLE OF CONTENTS
H H
Chapter – Significance: How Strong is the Evidence
W
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 0 – Two Quantitative Variables
W
Chapter 1 – Modeling Randomness
W
FOR INSTRUCTOR USE ONL
,Chapter W
Note: TE = Text entry TE-N = Text entry -
NumericMa = Matching MS = Multiple select
MC = Multiple choice TF = True-
False E = Easy, M = Medium, H = Hard
CHAPTER LEARNING OBJECTIVES
W
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 against the null hypothesis.
CLO1-
3: Calculate a standardized statistic for a single proportion and evaluate the strength ofev
idence it provides against a null hypothesis.
CLO1-
4: Describe how the distance of the observed statistic from the parameter value specifiedby t
he 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: Introduction to Chance Models
W
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 thec
hance model is a plausible explanation for the data.
LO1.1-
5: Implement the 3S strategy: find a statistic, simulate results from a chance model, andco
mment 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 thecor
rect explanation for the observed data.
FOR INSTRUCTOR USE ONL
, 1-2 Test Bank for Introduction to Statistical Investigations, 2nd Edition
Questions through 4:
W
Do red uniform wearers tend to win more often than those wearing blue uniforms in Taekw
ondo matches where competitors are randomly assigned to wear either a red or blue unifor
m? In a sample of 80 Taekwondo matches, there were 45 matches where thered uniform w
earer 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 T
aekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: A; LO: .1-1; Difficulty: Easy; Type: MC
W
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 T
aekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: B; LO: .1-1; Difficulty: Easy; Type: MC
W
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 stat
istically 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 thed
otplot above 40.
C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform wasd
FOR INSTRUCTOR USE ONL
Introduction to Statistical Investigations,
2nd Edition Nathan Tintle; Beth L. Chance
Chapters - 1, Complete
W W
FOR INSTRUCTOR USE ONL
,TABLE OF CONTENTS
H H
Chapter – Significance: How Strong is the Evidence
W
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 0 – Two Quantitative Variables
W
Chapter 1 – Modeling Randomness
W
FOR INSTRUCTOR USE ONL
,Chapter W
Note: TE = Text entry TE-N = Text entry -
NumericMa = Matching MS = Multiple select
MC = Multiple choice TF = True-
False E = Easy, M = Medium, H = Hard
CHAPTER LEARNING OBJECTIVES
W
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 against the null hypothesis.
CLO1-
3: Calculate a standardized statistic for a single proportion and evaluate the strength ofev
idence it provides against a null hypothesis.
CLO1-
4: Describe how the distance of the observed statistic from the parameter value specifiedby t
he 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: Introduction to Chance Models
W
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 thec
hance model is a plausible explanation for the data.
LO1.1-
5: Implement the 3S strategy: find a statistic, simulate results from a chance model, andco
mment 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 thecor
rect explanation for the observed data.
FOR INSTRUCTOR USE ONL
, 1-2 Test Bank for Introduction to Statistical Investigations, 2nd Edition
Questions through 4:
W
Do red uniform wearers tend to win more often than those wearing blue uniforms in Taekw
ondo matches where competitors are randomly assigned to wear either a red or blue unifor
m? In a sample of 80 Taekwondo matches, there were 45 matches where thered uniform w
earer 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 T
aekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: A; LO: .1-1; Difficulty: Easy; Type: MC
W
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 T
aekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: B; LO: .1-1; Difficulty: Easy; Type: MC
W
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 stat
istically 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 thed
otplot above 40.
C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform wasd
FOR INSTRUCTOR USE ONL
