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Test Bank for Introduction to Statistical Investigations 2nd Edition by Beth L. Chance – Complete Exam Prep & Practice Questions

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Prepare for your exams with confidence using this comprehensive Test Bank for "Introduction to Statistical Investigations" 2nd Edition by Beth L. Chance, designed to help you master every core concept and pass your course with flying colors. This essential study resource provides a massive collection of practice exam questions covering all chapters, from chance models and p-values to confidence intervals, chi-square tests, and linear regression, ensuring you are fully prepared for any question that comes your way. Each question is meticulously crafted to mirror the format and difficulty of actual exams, featuring a mix of multiple choice, true/false, matching, and free-response questions accompanied by detailed answer keys and learning objective references. Whether you are a student looking to test your knowledge, a tutor seeking reliable material, or an instructor preparing course assessments, this test bank is the ultimate tool to reinforce your understanding of statistical investigations and guarantee academic success.

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@PROFDOCDIGITALLIBRARIES




TEST BANK




TEST BANK

,@PROFDOCDIGITALLIBRARIES




Chapter 1
Introduction to Statistical Investigations Test Bank

Note: TE = Text entry TE-N = Text entry - Numeric
Ma = Matching MS = Multiple select
MC = Multiple choice TF = True-False

E = 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 against
the 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 specified
by 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 the
correct explanation for the observed data.

,
, @PROFDOCDIGITALLIBRARIES


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 the
red 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 wearer
wins
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 wearer
wins
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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