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

2.1 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

3.1 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

4.1 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

5.1 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

6.1 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

7.1 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

8.1 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

9.1 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

10.1 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

11.1 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

12.1 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

13.1 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.

The new ISI zyBook takes the author’s SBI approach to learning statistics into a new course management platform that allows students to interact with assignable reading made up of embedded in the text guided animations, simulation tools, and learning questions with answer-specific feedback, which work together to build confidence and conceptual understanding of the statistical process, while providing instructors with detailed student performance analytics. Challenge Activities deliver higher stakes assessment.

## The zyBooks Approach

zyBooks utilize the “Say, Show, Ask” approach.

Say: We use the trusted content of the textbook to explain concepts and teach students subject matter.

Show: Through animations and learning questions students can see concepts come to life.

Ask: Our built-in learning questions and homework with instant feedback encourage interactivity.

## Authors

Nathan Tintle / Professor, Statistics / Dordt University
Beth Chance / Professor, Statistics / California Polytechnic University
George Cobb / Robert L. Rooke Professor Emeritus, Statistics / Mount Holyoke College
Allan Rossman / Professor and Department Chair, Statistics / California Polytechnic University
Soma Roy / Professor, Statistics / California Polytechnic University
Todd Swanson / Associate Professor, Mathematics and Statistics / Hope College
Jill VanderStoep / Assistant Professor, Mathematics and Statistics / Hope College

## zyBooks authors

Julia Schedler / Content Lead, Statistics / zyBooks / PhD in Statistics / Rice University
Ayla Sánchez / Senior Content Developer, Statistics / zyBooks / PhD in Mathematics / Tufts University

## Key authoring contributors

Anelise Guimaraes Sabbag / Assistant Professor, Statistics / California Polytechnic University
Zoe Fox / Associate Content Developer, Statistics / zyBooks / BSc in Mathematics and Statistics / University of British Columbia
Pamela Fellers / Content Developer, Statistics / zyBooks / PhD in Statistics / University of Nebraska – Lincoln

## Combine Introduction to Statistical InvestigationsWith These Other zyBooks

Introduction to Statistical Investigations is often combined with other zyBooks to give students experience with closely related concepts. Some popular titles to pair with Introduction to Statistical Investigations include: