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

TESTBANK
IntroductiontoStatisticalInvestigations,
2ndEditionNathanTintle;BethL.Chance
Chapters1-11,Complete




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Chapter 1 – Significance: How Strong is the Evidence
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Chapter 2 – wq wq




Generalization: How Broadly Do the Results Apply? Chapter 3
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– Estimation: How Large is the Effect?
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Chapter 4 – wq wq




Causation: Can We Say What Caused the Effect? Chapter 5 –
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wq Comparing Two Proportions wq wq




Chapter 6 – Comparing Two Means
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Chapter 7 – wq wq




Paired Data: One Quantitative Variable Chapter 8 –
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Comparing More Than Two Proportions Chapter 9 –
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wq Comparing More Than Two Means Chapter 10 –
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wq Two Quantitative Variables
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Chapter 11 – Modeling Randomness
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t MC = Multiple choice
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FalseE = Easy, M = Medium, H = Hard
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CHAPTER 1 LEARNING OBJECTIVES wq wq wq




CLO1-
1: Use the chance model to determine whether an observed statistic is unlikely to occu
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r. CLO1-2: Calculate and interpret a p-
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value, and state the strength of evidence it provides againstthe null hypothesis.
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CLO1-
3: Calculate a standardized statistic for a single proportion and evaluate the strength ofe
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vide nce 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
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the null hypothesis, sample size, and one- vs. two-
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sided tests affect the 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
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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
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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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, 4: Identi d a statistic, simulate results from a chance model, andcomm ent on strength of evidence
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fy wheth against observed study results happening by chance alone.
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esults ar 6: Differentiate between saying the chance model is plausible and the chance model is th
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e statisti ecorrec t explanation for the observed data.
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