https://www.stuvia.com/user/lectkeshi
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 -
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
FOR INSTRUCTOR USE ONLY
, 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.
FOR INSTRUCTOR USE ONLY
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 -
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
FOR INSTRUCTOR USE ONLY
, 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.
FOR INSTRUCTOR USE ONLY
