Introduction to Statistical Investigations – 2nd Edition by Nathan Tintle & Beth L. Chance | Test Bank Chapters 1–11 Complete

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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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ss 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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ss 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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ss Chapter 8 – Comparing More Than Two Proportions
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ss Chapter 9 – Comparing More Than Two Means Chapter
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ss 10 – Two Quantitative Variables
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Chapter 11 – Modeling Randomness
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,Chapter 1
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ss Numeric Ma nn s s = Matching MS s s = Multiple s s




s s select MC ss s s = Multiple choice s s TF = True- ss s s




FalseE

= Easy, M = Medium, H = Hard
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CHAPTER 1 LEARNING OBJECTIVES ss ss ss




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
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against the null hypothesis.
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CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength
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ofevidence it provides against a null hypothesis.
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CLO1-4: Describe how the distance of the observed statistic from the parameter value
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specified by the null hypothesis, sample size, and one- vs. two-sided tests affect the
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strength of evidence 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 ss ss ss ss ss




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
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the ran- dom 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
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not thechance 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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andcomment on strength of evidence against observed study results happening by chance
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alone. ss




LO1.1-6: s s Differentiate between saying the chance model s s s s s s s s s s s s is s s plausible s s and s s the s s chance
s s model is thecorrect explanation for the observed data.
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, 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
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in Taekwondo matches where competitors are randomly assigned to wear either a red
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or blue uniform? In a sample of 80 Taekwondo matches, there were 45 matches
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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
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wearerwins s s




B. The proportion of matches in which the red uniform wearer wins in a
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sample of 80Taekwondo matches
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C. Whether the red uniform wearer wins a match ss ss ss ss s s s s ss




D. 0.50
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
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wearerwins s s




B. The proportion of matches in which the red uniform wearer wins in a
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sample of 80Taekwondo matches
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C. Whether the red uniform wearer wins a match ss ss ss ss s s s s ss




D. 0.50
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
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bychance 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. ss s s s s s s ss ss




B. Yes, since the height of the dotplot above 45 is smaller than the
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height of the dotplot 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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