INVESTIGATIONSB 2ND EDITION BETH L CHANCE
TABLE OF CONTENTS
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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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FOR INSTRUCTOR USE ONLY
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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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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 c
Note: TE = Text entry
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c = Matching c MS = Multiple select c c c
MC = Multiple choice c c c TF = True-FalseE =
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c Easy, M = Medium, H = Hard c c c c c c
CHAPTER 1 LEARNING OBJECTIVES c c c
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.c
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.
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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 c c c c c
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, andcomment
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on strength of evidence against observed study results happening by chance alone.
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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 c c c
, 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.
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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 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 c c c
C. Whether the red uniform wearer wins a match c c c c c c c
D. 0.50 c
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
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80Taekwondo matches c c c
C. Whether the red uniform wearer wins a match c c c c c c c
D. 0.50 c
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 bychance
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alone in a sample of 80 matches. Based on this simulation, is our observed result statistically
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significant?
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A. Yes, since 45 is larger than 40. c c c c c c
B. Yes, since the height of the dotplot above 45 is smaller than the height of thedotplot
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above 40. c c
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 c c c