TEST BANK For Introduction to Statistical Investigations, 2nd Edition by Nathan Tintle; Beth L. Chance, Verified Chapters 1 - 11 ||Complete A+ Guide

TEST BANK m m




Introduction to Statistical Investigations,
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2nd Edition Nathan Tintle; Beth L. Chance
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m Chapters 1 - 11, Complete
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,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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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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,Chapter 1 m




Note: mmm TE = m m Text entry m TE-N = Text entry - Numeric Ma
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m = m Matching MS = Multiple select
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MC = m m Multiple choice m TF = True-False E =
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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 against the null
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hypothesis.
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CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength of evidence it
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provides against a null hypothesis.
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CLO1-4: Describe how the distance of the observed statistic from the parameter value specified by 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
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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 choice
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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 chance
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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, and comment
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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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, 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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mmatches where competitors are randomly assigned to wear either a red or blue uniform? In a
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msample of 80 Taekwondo matches, there were 45 matches where the red 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 wins
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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
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D. 0.50 m



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 wins
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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
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D. 0.50 m



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 chance
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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.
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B. Yes, since the height of the dotplot above 45 is smaller than the height of the dotplot
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m above 40. m



C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform was
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determined by chance alone.
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