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 G
Note: TE =
GGG G G Text entry G TE-N = Text entry - NumericMa
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G = G Matching MS = Multiple select
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MC = Multiple choice
G G G TF = True-FalseE =
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G Easy, M = Medium, H = Hard
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CHAPTER 1 LEARNING OBJECTIVES
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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
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null hypothesis.
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CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength of
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evidence 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
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null hypothesis, sample size, and one- vs. two-sided tests affect the strength of evidence
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against the null hypothesis.
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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 G G G G G
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 the
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chance 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, and
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comment on strength of evidence against observed study results happening by chance
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alone.
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LO1.1-6: Differentiate between saying the chance model is plausible and the chance model is the
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correct explanation for the observed data.
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FOR INSTRUCTOR USE ONLY
, 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
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Taekwondo matches where competitors are randomly assigned to wear either a red or blue
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uniform? In a sample of 80 Taekwondo matches, there were 45 matches where thered
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uniform wearer won.
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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 wearer
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wins G
B. The proportion of matches in which the red uniform wearer wins in a sample of 80
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Taekwondo matches
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C. Whether the red uniform wearer wins a match G G G G G G G
D. 0.50 G
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 wearer
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wins G
B. The proportion of matches in which the red uniform wearer wins in a sample of 80
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Taekwondo matches
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C. Whether the red uniform wearer wins a match G G G G G G G
D. 0.50 G
Ans: B; LO: 1.1-1; Difficulty: Easy; Type: MC
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3. Given below is the simulated distribution of the number of ―red wins‖ that could happen by
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chance alone in a sample of 80 matches. Based on this simulation, is our observed result
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statistically significant?
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A. Yes, since 45 is larger than 40.
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B. Yes, since the height of the dotplot above 45 is smaller than the height of the
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dotplot above 40.
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C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform was
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FOR INSTRUCTOR USE ONLY