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

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

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TEST BANK b b




Introduction to Statistical Investigations,
b b b b




2nd Edition Nathan Tintle; Beth L. Chance
b b b b b b




b Chapters 1 - 11, Complete
b b b b




TABLE OF CONTENTS
b b b




FOR INSTRUCTOR USE ONLY
b b b

,Chapter 1 – Significance: How Strong is the Evidence
b b b b b b b b b




Chapter 2 – Generalization: How Broadly Do the Results Apply?
b b b b b b b b b b




Chapter 3 – Estimation: How Large is the Effect?
b b b b b b b b b




Chapter 4 – Causation: Can We Say What Caused the Effect?
b b b b b b b b b b b




Chapter 5 – Comparing Two Proportions
b b b b b b




Chapter 6 – Comparing Two Means
b b b b b b




Chapter 7 – Paired Data: One Quantitative Variable
b b b b b b b b




Chapter 8 – Comparing More Than Two Proportions
b b b b b b b b




Chapter 9 – Comparing More Than Two Means
b b b b b b b b




Chapter 10 – Two Quantitative Variables
b b b b b b




Chapter 11 – Modeling Randomness
b b b b




FOR INSTRUCTOR USE ONLY
b b b

,Chapter 1 b




Note: TE = Text entry
bb b b b b TE-N = Text entry - NumericMa
b b b b b b




b = Matching b MS = Multiple select b b b




MC = Multiple choice b b b TF = True-FalseE =
b b b b




b Easy, M = Medium, H = Hard b b b b b b




CHAPTER 1 LEARNING OBJECTIVES b b b




CLO1-1: Use the chance model to determine whether an observed statistic is unlikely to occur.
b b b b b b b b b b b b b b




CLO1-2: Calculate and interpret a p-value, and state the strength of evidence it provides againstthe null
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hypothesis. b




CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength ofevidence 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 specifiedby the null
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hypothesis, sample size, and one- vs. two-sided tests affect the strength of evidence against the null
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hypothesis. b




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 b b b b b




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.
b b b




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, 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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FOR INSTRUCTOR USE ONLY b b b

, 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 80
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Taekwondo matches b b




C. Whether the red uniform wearer wins a match b b b b b b b




D. 0.50 b




Ans: A; LO: 1.1-1; Difficulty: Easy; Type: MC
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2. What is the statistic for this study?
b b b b b b




A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
b b b b b b b b b b b b




B. The proportion of matches in which the red uniform wearer wins in a sample of 80
b b b b b b b b b b b b b b b




Taekwondo matches b b




C. Whether the red uniform wearer wins a match b b b b b b b




D. 0.50 b




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?
b




A. Yes, since 45 is larger than 40. b b b b b b




B. Yes, since the height of the dotplot above 45 is smaller than the height of thedotplot
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above 40. b b




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.
b b b b




FOR INSTRUCTOR USE ONLY b b b
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