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

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IntroductiontoStatisticalInvestigations,
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2ndEditionNathanTintle;BethL.Chance
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r Chapters1-11,Completer r r r




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,TABLEOFCONTENTS
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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 ThanTwo Proportions
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Chapter 9 – Comparing More ThanTwo Means
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Chapter 10 – Two Quantitative Variables
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Chapter 11 – Modeling Randomness
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,Chapter 1 r




Note: TE = Text entry
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r Matching MS = Multiple select r r r




MC = Multiple choice r r r TF = True-FalseE =
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r Easy, M= Medium, H = Hard r r r r r r




CHAPTER 1LEARNING OBJECTIVES r r r




CLO1-1: Use thechance modeltodetermine whetheranobserved statisticis unlikely tooccur.
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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. r




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, andone- vs. two-sided testsaffect the strength of evidence against the null
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hypothesis. r




CLO1-5: Describe how to carryout a theory-based,one-proportion z-test.
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Section1.1: Introduction toChanceModels r r r r r




LO1.1-1: Recognizethedifferencebetweenparametersand statistics.
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LO1.1-2: Describe howto use cointossing to simulate outcomesfroma chance modelof the ran-dom choice
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between two events.
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LO1.1-3: UsetheOne Proportion applet to carry out thecoin 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 on
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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 Introductionto Statistical Investigations, 2nd Edition
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Questions1 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 sample
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of 80 Taekwondo matches, there were 45 matches where thered uniform wearer won.
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1. What is the parameterof interest forthis 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 80Taekwondo
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matches r




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




D. 0.50 r




Ans:A; LO: 1.1-1; Difficulty:Easy; Type:MC
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2. What is the statisticforthis 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 80Taekwondo
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matches r




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




D. 0.50 r




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 significant?
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A. Yes, since 45 is largerthan40. r r r r r r




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




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




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