Table of Contents
1. Introduction to Statistical Investigations
1.1 Preliminary: Introduction to the six-step method
1.2 Preliminary: Exploring data
1.3 Preliminary: Exploring random processes
1.4 Data and formulas
2. Significance
2.1 Example: Introduction to chance models
2.2 Example: Measuring the strength of evidence
2.3 Example: Alternative measure of strength of evidence
2.4 Example: What impacts strength of evidence?
2.5 Example: Inference on a single proportion: Theory-based approach
2.6 Supplemental Exploration: Introduction to chance models
2.7 Supplemental Exploration: Measuring the strength of evidence
2.8 Supplemental Exploration: Do People Use Facial Prototyping?
2.9 Supplemental Exploration: Competitive Advantage to Uniform Colors?
2.10 Supplemental Exploration: Eye Dominance
2.11 Investigation: Tire story falls flat
2.12 Tools, data, and formulas
3. Generalization
3.1 Example: Sampling from a finite population
3.2 Example: Inference for a single quantitative variable
3.3 Example: Theory-based Inference for a Population Mean
3.4 Example: Other Statistics
3.5 Supplemental Exploration: Sampling Words
3.6 Supplemental Exploration: Inference for a single quantitative variable
3.7 Supplemental Exploration: Sleepless Nights?
3.8 Supplemental Exploration: Other statistics
3.9 Investigation: Faking cell phone calls
3.10 Tools, data, and formulas
4. Estimation
4.1 Example: Statistical inference: Confidence intervals
4.2 Example: 2SD and theory-based confidence intervals for a single proportion
4.3 Example: 2SD and theory-based confidence intervals for a single mean
4.4 Example: Factors that affect the width of a confidence interval
4.5 Supplemental Exploration: Statistical inference: Confidence intervals
4.6 Supplemental Exploration: 2SD and theory-based confidence intervals for a single proportion
4.7 Supplemental Exploration: 2SD and theory-based confidence intervals for a single mean
4.8 Supplemental Exploration A: Factors that affect the width of a confidence interval
4.9 Supplemental Exploration B: Factors that affect the width of a confidence interval
4.10 Investigation: Cell phones while driving
4.11 Tools, data, and formulas
5. Causation
5.1 Example: Association and confounding
5.2 Example: Observational studies vs. experiments
5.3 Supplemental Exploration: Association and confounding
5.4 Supplemental Exploration: Observational studies versus experiments
5.5 Investigation: High anxiety and sexual attraction
5.6 Tools and data
6. Comparing Two Proportions
6.1 Example: Comparing two groups: Categorical response
6.2 Example: Comparing two proportions: Simulation-based approach
6.3 Example: Comparing two proportions: Theory-based approach
6.4 Supplemental Exploration: Comparing two groups: Categorical response
6.5 Supplemental Exploration: Comparing two proportions: Simulation-based approach
6.6 Supplemental Exploration: Comparing two proportions: Theory-based approach
6.7 Investigation: Does vitamin C improve your health?
6.8 Tools, data, and formulas
7. Comparing Two Means
7.1 Example: Comparing two groups: Quantitative response
7.2 Example: Comparing two means: Simulation-based approach
7.3 Example: Comparing two means: Theory-based approach
7.4 Supplemental Exploration: Comparing two groups: Quantitative response
7.5 Supplemental Exploration: Comparing two means: Simulation-based approach
7.6 Supplemental Exploration: Comparing two means: Theory-based approach
7.7 Investigation: Memorizing letters
7.8 Tools, data, and formulas
8. Paired Data
8.1 Example: Paired designs
8.2 Example: Simulation-based approach for analyzing paired data
8.3 Example: Theory-based approach to analyzing data from paired samples
8.4 Supplemental Exploration: Paired designs
8.5 Supplemental Exploration: Simulation-based approach for analyzing paired data
8.6 Supplemental Exploration: Theory-based approach for analyzing paired data
8.7 Investigation: Filtering water in Cameroon
8.8 Tools, data, and formulas
9. Comparing More Than Two Proportions
9.1 Example: Comparing multiple proportions: Simulation-based approach
9.2 Example: Comparing multiple proportions: Theory-based approach
9.3 Example: Chi-square goodness-of-fit test
9.4 Supplemental Exploration: Comparing multiple proportions: Simulation-based approach
9.5 Supplemental Exploration A: Comparing multiple proportions: Theory-based approach
9.6 Supplemental Exploration B: Comparing multiple proportions: Theory-based approach
9.7 Supplemental Exploration: Chi-square goodness-of-fit test
9.8 Investigation: Who yields to pedestrians?
9.9 Tools, data, and formulas
10. Comparing More Than Two Means
10.1 Example: Comparing multiple means: Simulation-based approach
10.2 Example: Comparing multiple means: Theory-based approach
10.3 Supplemental Exploration: Comparing multiple means: Simulation-based approach
10.4 Supplemental Exploration: Comparing multiple means: Theory-based approach
10.5 Investigation: Aggression
10.6 Tools, data, and formulas
11. Two Quantitative Variables
11.1 Example: Two quantitative variables: Scatterplot and correlation
11.2 Example: Inference for correlation coefficient: A simulation-based approach
11.3 Example: Least squares regression
11.4 Example: Inference for regression slope: Simulation-based approach
11.5 Example: Inference for regression slope: Theory-based approach
11.6 Supplemental Exploration: Two quantitative variables: Scatterplot and correlation
11.7 Supplemental Exploration: Inference for correlation coefficient: A simulation-based approach
11.8 Supplemental Exploration: Least squares regression
11.9 Supplemental Exploration: Inference for regression slope: Simulation-based approach
11.10 Supplemental Exploration: Inference for regression slope: Theory-based approach
11.11 Investigation: Association between hand span and candy?
11.12 Tools, data, and formulas
12. Modeling Randomness
12.1 Example: Basics of probability
12.2 Example: Probability rules
12.3 Example: Conditional probability and independence
12.4 Example: Discrete random variables
12.5 Example: Random variable rules
12.6 Example: Binomial and geometric random variables
12.7 Example: Continuous random variables and normal distribution
12.8 Example: Revisiting theory-based approximations of sampling distributions
12.9 Supplemental Exploration: Basics of probability
12.10 Supplemental Exploration: Probability rules
12.11 Supplemental Exploration A: Conditional probability and independence
12.12 Supplemental Exploration B: Conditional probability and independence
12.13 Supplemental Exploration: Discrete random variables
12.14 Supplemental Exploration: Random variable rules
12.15 Supplemental Exploration: Binomial and geometric random variables
12.16 Supplemental Exploration A: Continuous random variables and normal distribution
12.17 Supplemental Exploration B: Continuous random variables and normal distribution
12.18 Supplemental Exploration A: Revisiting theory-based approximations of sampling distributions
12.19 Supplemental Exploration B: Revisiting theory-based approximations of sampling distributions
13. Instructor Resources
13.1 Under the Spiral: How the ISI zyBook teaches the Statistical Investigation Process
13.2 Examples and Explorations
What You’ll Find In This zyBook:
More action with less text.
- Bring text authors’ SBI approach to learning statistics into a new course management platform
- Students interact with assignable reading made up of embedded in-the-text guided animations, simulation tools, and learning questions with answer-specific feedback
- Build confidence and conceptual understanding of the statistical investigation process
- Challenge Activities deliver higher-stakes assessment
- Adopters have access to a test bank with questions for every chapter
Scaffolding in the ISI zyBook:
What is a zyBook?
Introduction to Statistical Investigations is a web-native, interactive zyBook that helps students visualize concepts to learn faster and more effectively than with a traditional textbook. (Check out our research.)
Since 2012, over 1,700 academic institutions have adopted digital zyBooks to transform their STEM education.
zyBooks benefit both students and instructors:
- Instructor benefits
- Customize your course by reorganizing existing content, or adding your own content
- Continuous publication model updates your course with the latest content and technologies
- Robust reporting gives you insight into students’ progress, reading and participation
- Save time with auto-graded labs and challenge activities that seamlessly integrate with your LMS gradebook
- Build quizzes and exams with hundreds of included test questions
- Student benefits
- Learning questions and other content serve as an interactive form of reading
- Instant feedback on labs and homework
- Concepts come to life through extensive animations embedded into the interactive content
- Review learning content before exams with different questions and challenge activities
- Save chapters as PDFs to reference the material at any time
Authors
Nathan Tintle / Professor, Statistics, University of Illinois Chicago
Beth Chance / Professor, Statistics, California Polytechnic State University
George Cobb / Robert L. Rooke Professor Emeritus, Statistics, Mount Holyoke College
Allan Rossman / Professor, Statistics, California Polytechnic State University
Soma Roy / Professor, Statistics, California State Polytechnic University
Todd Swanson / Associate Professor, Mathematics and Statistics, Hope College
Jill VanderStoep / Assistant Professor, Mathematics and Statistics, Hope College
zyBooks Authors
Julia Schedler / PhD in Statistics, Rice University
Ayla Sánchez / Senior Content Developer, Statistics / PhD in Mathematics, Tufts University
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