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Test Bank for Introduction To Statistical Investigations 2nd Edition. Chapter 1-11 Complete.

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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 Randomness FOR INSTRUCTOR USE ONLY FOR INSTRUCTOR USE ONLY Chapter 1 Note: TE = Text entry TE-N = Text entry - NumericMa = Matching MS = Multiple select MC = Multiple choice TF = True-FalseE = Easy, M = Medium, H = Hard CHAPTER 1 LEARNING OBJECTIVES CLO1-1: Use the chance model to determine whether an observed statistic is unlikely to occur. CLO1-2: Calculate and interpret a p-value, and state the strength of evidence it provides againstthe null hypothesis. CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength of evidence it provides against a null hypothesis. CLO1-4: Describe how the distance of the observed statistic from the parameter value specifiedby the null hypothesis, sample size, and one- vs. two-sided tests affect the strength of evidence against the null hypothesis. CLO1-5: Describe how to carry out a theory-based, one-proportion z-test. Section 1.1: Introduction to Chance Models LO1.1-1: Recognize the difference between parameters and statistics. LO1.1-2: Describe how to use coin tossing to simulate outcomes from a chance model of the ran-dom choice between two events. LO1.1-3: Use the One Proportion applet to carry out the coin tossing simulation. LO1.1-4: Identify whether or not study results are statistically significant and whether or not the chance model is a plausible explanation for the data. LO1.1-5: Implement the 3S strategy: find a statistic, simulate results from a chance model, and comment on strength of evidence against observed study results happening by chance alone. LO1.1-6: Differentiate between saying the chance model is plausible and the chance model is thecorrect explanation for the observed data. FOR INSTRUCTOR USE ONLY 1-2 Test Bank for Introduction to Statistical Investigations, 2nd Edition Questions 1 through 4: Do red uniform wearers tend to win more often than those wearing blue uniforms in Taekwondo matches where competitors are randomly assigned to wear either a red or blue uniform? In a sample of 80 Taekwondo matches, there were 45 matches where thered uniform wearer won. 1. What is the parameter of interest for this study? A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins B. The proportion of matches in which the red uniform wearer wins in a sample of 80 Taekwondo matches C. Whether the red uniform wearer wins a match D. 0.50 Ans: A; LO: 1.1-1; Difficulty: Easy; Type: MC 2. What is the statistic for this study? A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins B. The proportion of matches in which the red uniform wearer wins in a sample of 80 Taekwondo matches C. Whether the red uniform wearer wins a match D. 0.50 Ans: B; LO: 1.1-1; Difficulty: Easy; Type: MC 3. Given below is the simulated distribution of the number of ―red wins‖ that could happen by chance alone in a sample of 80 matches. Based on this simulation, is our observed result statistically significant? A. Yes, since 45 is larger than 40. B. Yes, since the height of the dotplot above 45 is smaller than the height of the dotplot above 40. C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform was determined by chance alone. FOR INSTRUCTOR USE ONLY Introduction to Financial Statements 1-3 D. No, since we could have observed a value greater than 45 just by random chance. Ans: C; LO: 1.1-4; Difficulty: Medium; Type: MC 4. What can we conclude from the results of this study? Select all that apply. A. The results of this study are something that could easily have happened if thecolor of the winner‘s uniform was determined by chance alone. B. We do not have convincing evidence against the ―by-chance-alone‖ model. C. The results of this study prove that the color of the winner‘s uniform was determined by chance alone. D. We do not have convincing evidence that red uniform wearers tend to win moreoften than those wearing blue uniforms. Ans: A, B, D; LO: 1.1-6; Difficulty: Hard; Type: MS Questions 5 through 8: Suppose you are testing to see if your dog, Hope, understands pointing towards an object. You place two objects about 2.5 meters away, then you point towards one of the objects. In 20 trials,Hope goes to the correct object 13 times (or 65%). 5. Fill in the blanks with the correct One Proportion applet inputs to carry out an appropriate simulation of this process, if Hope does not understand pointing towards an object and isjust guessing. Probability of success: Sample size: Number of samples: Ans: 0.5 (Tol: 0), 20 (Tol: 0), Any integer as larger or larger than 1000; LO: 1.1-3; Difficulty: Easy; Type: TE-N 6. Match the parts of the real study corresponding to the physical (coin-flipping) simulation: Coin flip = Heads = Tails = Chance of heads = One repetition = A. 0.5, probability of Hope going to the correct object B. Hope going to the correct object C. Hope going to the incorrect object D. One set of 20 attempts by Hope E. Hope going to an object Ans: E, B, C, A, D; LO: 1.1-2; Difficulty: Medium; Type: Ma 1-4 Test Bank for Introduction to Statistical Investigations, 2nd Edition

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Institución
Introduction To Statistical Investigations
Grado
Introduction To Statistical Investigations

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Subido en
28 de septiembre de 2025
Número de páginas
347
Escrito en
2025/2026
Tipo
Examen
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TEST BANK
Introduction to Statistical Investigations,
2nd Edition Nathan Tintle; Beth L. Chance
Chapters 1 - 11, Complete




FOR INSTRUCTOR USE ONLY

,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 Randomness




FOR INSTRUCTOR USE ONLY

,Chapter 1
Note: TE = Text entry TE-N = Text entry - NumericMa
= Matching MS = Multiple select
MC = Multiple choice TF = True-FalseE =

Easy, M = Medium, H = Hard


CHAPTER 1 LEARNING OBJECTIVES
CLO1-1: Use the chance model to determine whether an observed statistic is unlikely to occur.
CLO1-2: Calculate and interpret a p-value, and state the strength of evidence it provides againstthe null
hypothesis.
CLO1-3: Calculate a standardized statistic for a single proportion and evaluate the strength of
evidence it provides against a null hypothesis.
CLO1-4: Describe how the distance of the observed statistic from the parameter value specifiedby the
null hypothesis, sample size, and one- vs. two-sided tests affect the strength of evidence against
the null hypothesis.
CLO1-5: Describe how to carry out a theory-based, one-proportion z-test.


Section 1.1: Introduction to Chance Models
LO1.1-1: Recognize the difference between parameters and statistics.
LO1.1-2: Describe how to use coin tossing to simulate outcomes from a chance model of the ran-dom
choice between two events.
LO1.1-3: Use the One Proportion applet to carry out the coin tossing simulation.
LO1.1-4: Identify whether or not study results are statistically significant and whether or not the
chance model is a plausible explanation for the data.
LO1.1-5: Implement the 3S strategy: find a statistic, simulate results from a chance model, and
comment on strength of evidence against observed study results happening by chance alone.
LO1.1-6: Differentiate between saying the chance model is plausible and the chance model is thecorrect
explanation for the observed data.




FOR INSTRUCTOR USE ONLY

, 1-2 Test Bank for Introduction to Statistical Investigations, 2nd Edition


Questions 1 through 4:
Do red uniform wearers tend to win more often than those wearing blue uniforms in
Taekwondo matches where competitors are randomly assigned to wear either a red or blue
uniform? In a sample of 80 Taekwondo matches, there were 45 matches where thered uniform
wearer won.
1. What is the parameter of interest for this study?
A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
B. The proportion of matches in which the red uniform wearer wins in a sample of 80
Taekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: A; LO: 1.1-1; Difficulty: Easy; Type: MC
2. What is the statistic for this study?
A. The long-run proportion of Taekwondo matches in which the red uniform wearerwins
B. The proportion of matches in which the red uniform wearer wins in a sample of 80
Taekwondo matches
C. Whether the red uniform wearer wins a match
D. 0.50
Ans: B; LO: 1.1-1; Difficulty: Easy; Type: MC
3. Given below is the simulated distribution of the number of ―red wins‖ that could happen by
chance alone in a sample of 80 matches. Based on this simulation, is our observed result
statistically significant?




A. Yes, since 45 is larger than 40.
B. Yes, since the height of the dotplot above 45 is smaller than the height of the
dotplot above 40.
C. No, since 45 is a fairly typical outcome if the color of the winner‘s uniform was
determined by chance alone.

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