Table of Contents

   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 ‚Äč‚ÄčExploration: 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 ‚Äč‚ÄčSupplemental Exploration: Basics of probability

¬†¬†¬†12.10 ‚Äč‚ÄčSupplemental Exploration: Probability rules

¬†¬†¬†12.11 ‚Äč‚ÄčSupplemental Exploration A: Conditional probability and independence

¬†¬†¬†12.12 ‚Äč‚ÄčSupplemental Exploration B: Conditional probability and independence

¬†¬†¬†12.13 ‚Äč‚ÄčSupplemental Exploration: Discrete random variables

¬†¬†¬†12.14 ‚Äč‚ÄčSupplemental Exploration: Random variable rules

¬†¬†¬†12.15 ‚Äč‚ÄčSupplemental Exploration: Binomial and geometric random variables

¬†¬†¬†12.16 ‚Äč‚ÄčSupplemental Exploration A: Continuous random variables and normal distribution

¬†¬†¬†12.17 ‚Äč‚ÄčSupplemental Exploration B: Continuous random variables and normal distribution

¬†¬†¬†12.18 ‚Äč‚ÄčSupplemental Exploration A: Revisiting theory-based approximations of sampling distributions

¬†¬†¬†12.19 ‚Äč‚ÄčSupplemental 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.

  • 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.

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  • 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
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  • Concepts come to life through extensive animations embedded into the interactive content
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  • 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 S√°nchez / Senior Content Developer, Statistics / PhD in Mathematics, Tufts University

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