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# Introductory Statistics: A Student-Centered Approach

## First Edition| ©2024 Ann Cannon; Daniel Starnes; Joshua Tabor

Introductory Statistics: A Student-Centered Approach and online resources in Achieve provide the keys to understanding statistics in the real world for today’s students. The authors have taken their streamlined writing style and layered in a wealth of worked examples, real data and applications,

Introductory Statistics: A Student-Centered Approach and online resources in Achieve provide the keys to understanding statistics in the real world for today’s students. The authors have taken their streamlined writing style and layered in a wealth of worked examples, real data and applications, and easily accessible resources for making sense of statistics in our everyday lives. Knowledge and skills in statistics should be attainable for all students, no matter their background, and Introductory Statistics: A Student-Centered Approach balances the skill building and straightforward conceptual understanding students need to succeed.

The digital resources in Achieve connect the mission of the text to immersive learning experiences and multimedia with a variety of assessment types to fit any teaching style. For courses that incorporate statistical software, Tech Corners in the text and in Achieve encourage students to analyze real-world data with the software of their choice.

Achieve and Introductory Statistics: A Student-Centered Approach started first with the needs of students–how they learn and digest new material, what’s relevant to them, and how they practice their skills–and built out an all-in-one offering designed to provide a pathway to understanding for students of all backgrounds.

ISBN:9781319497576

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ISBN:9781319497583

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Save money with our hole-punched, loose-leaf textbook.

ISBN:9781319554255

This package includes Achieve and Loose-Leaf.

Introductory Statistics: A Student-Centered Approach and online resources in Achieve provide the keys to understanding statistics in the real world for today’s students. The authors have taken their streamlined writing style and layered in a wealth of worked examples, real data and applications, and easily accessible resources for making sense of statistics in our everyday lives. Knowledge and skills in statistics should be attainable for all students, no matter their background, and Introductory Statistics: A Student-Centered Approach balances the skill building and straightforward conceptual understanding students need to succeed.

The digital resources in Achieve connect the mission of the text to immersive learning experiences and multimedia with a variety of assessment types to fit any teaching style. For courses that incorporate statistical software, Tech Corners in the text and in Achieve encourage students to analyze real-world data with the software of their choice.

Achieve and Introductory Statistics: A Student-Centered Approach started first with the needs of students–how they learn and digest new material, what’s relevant to them, and how they practice their skills–and built out an all-in-one offering designed to provide a pathway to understanding for students of all backgrounds.

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New to This Edition

**
Introductory Statistics: A Student-Centered Approach**

First Edition| ©2024

Ann Cannon; Daniel Starnes; Joshua Tabor

# Digital Options

## Achieve

Achieve is a comprehensive set of interconnected teaching and assessment tools that incorporate the most effective elements from Macmillan Learning's market leading solutions in a single, easy-to-use platform.

## E-book

Read online (or offline) with all the highlighting and notetaking tools you need to be successful in this course.

**Introductory Statistics: A Student-Centered Approach**

First Edition| 2024

Ann Cannon; Daniel Starnes; Joshua Tabor

## Table of Contents

Chapter 1 Collecting DataSection 1.1 Introduction to Data Collection

Section 1.2 Sampling: Good and Bad

Section 1.3 Simple Random Sampling

Section 1.4 Other Sampling Methods

Section 1.5 Observational Studies and Experiments

Section 1.6 Completely Randomized Designs

Section 1.7 Blocking

Section 1.8 Data Ethics and the Scope of Inference

Chapter 2 Displaying Data with Graphs

Section 2.1 Displaying Categorical Data

Section 2.2 Displaying Relationships Between Two Categorical Variables

Section 2.3 Displaying Quantitative Data: Dotplots

Section 2.4 Displaying Quantitative Data: Stemplots

Section 2.5 Displaying Quantitative Data: Histograms

Section 2.6 Displaying Relationships Between Two Quantitative Variables

Chapter 3 Numerical Summaries for Quantitative Data

Section 3.1 Measuring Center

Section 3.2 Measuring Variability

Section 3.3 Boxplots and Outliers

Section 3.4 Measuring Location in a Distribution

Section 3.5 Relationships Between Two Variables: Correlation

Section 3.6 More About Correlation

Chapter 4 Probability

Section 4.1 Randomness, Probability, and Simulation

Section 4.2 Basic Probability Rules

Section 4.3 Two-Way Tables and Venn Diagrams

Section 4.4 Conditional Probability and Independence

Section 4.5 The General Multiplication Rule and Bayes’ Theorem

Section 4.6 The Multiplication Rule for Independent Events

Section 4.7 The Multiplication Counting Principle and Permutations

Section 4.8 Combinations and Probability

Chapter 5 Discrete Random Variables

Section 5.1 Introduction to Random Variables

Section 5.2 Analyzing Discrete Random Variables

Section 5.3 Binomial Random Variables

Section 5.4 Analyzing Binomial Random Variables

Section 5.5 Poisson Random Variables

Chapter 6 Normal Distributions and Sampling Distributions

Section 6.1 Continuous Random Variables

Section 6.2 Normal Distributions: Finding Areas from Values

Section 6.3 Normal Distributions: Finding Values from Areas

Section 6.4 Normal Approximation to the Binomial Distribution and Assessing Normality

Section 6.5 Sampling Distributions

Section 6.6 Sampling Distributions: Bias and Variability

Section 6.7 Sampling Distribution of the Sample Proportion

Section 6.8 Sampling Distribution of the Sample Mean and the Central Limit Theorem

Chapter 7 Estimating a Parameter

Section 7.1 The Idea of a Confidence Interval

Section 7.2 Factors That Affect the Margin of Error

Section 7.3 Estimating a Population Proportion

Section 7.4 Confidence Intervals for a Population Proportion

Section 7.5 Estimating a Population Mean

Section 7.6 Confidence Intervals for a Population Mean

Section 7.7 Estimating a Population Standard Deviation or Variance

Section 7.8 Confidence Intervals for a Population Standard Deviation or Variance

Chapter 8 Testing a Claim

Section 8.1 The Idea of a Significance Test

Section 8.2 Significance Tests and Decision Making

Section 8.3 Testing a Claim About a Population Proportion

Section 8.4 Significance Tests for a Population Proportion

Section 8.5 Testing a Claim About a Population Mean

Section 8.6 Significance Tests for a Population Mean

Section 8.7 Power of a Test

Section 8.8 Significance Tests for a Population Standard Deviation or Variance

Chapter 9 Comparing Two Populations or Treatments

Section 9.1 Confidence Intervals for a Difference Between Two Population Proportions

Section 9.2 Significance Tests for a Difference Between Two Population Proportions

Section 9.3 Confidence Intervals for a Difference Between Two Population Means

Section 9.4 Significance Tests for a Difference Between Two Population Means

Section 9.5 Analyzing Paired Data: Confidence Intervals for a Population Mean Difference

Section 9.6 Significance Tests for a Population Mean Difference

Section 9.7 Significance Tests for Two Population Standard Deviations or Variances

Chapter 10 Chi-Square and Analysis of Variance (ANOVA)

Section 10.1 Testing the Distribution of a Categorical Variable in a Population

Section 10.2 Chi-Square Tests for Goodness of Fit

Section 10.3 Testing the Relationship Between Two Categorical Variables in a Population

Section 10.4 Chi-Square Tests for Association

Section 10.5 Introduction to Analysis of Variance

Section 10.6 One-Way Analysis of Variance

Chapter 11 Linear Regression

Section 11.1 Regression Lines

Section 11.2 The Least-Squares Regression Line

Section 11.3 Assessing a Regression Model

Section 11.4 Confidence Intervals for the Slope of a Population Least-Squares Regression Line

Section 11.5 Significance Tests for the Slope of a Population Least-Squares Regression Line

Section 11.6 Confidence Intervals for a Mean Response and Prediction Intervals in Regression

Chapter 12 Multiple Regression

Section 12.1 Introduction to Multiple Regression

Section 12.2 Indicator Variables and Interaction

Section 12.3 Inference for Multiple Regression

Chapter 13 Nonparametric Methods

Section 13.1 The Sign Test

Section 13.2 The Wilcoxon Signed Rank Test

Section 13.3 The Wilcoxon Rank Sum Test

Section 13.4 The Kruskal-Wallis Test

Section 13.5 Randomization Tests

Section 13.6 Bootstrapping

**Introductory Statistics: A Student-Centered Approach**

First Edition| 2024

Ann Cannon; Daniel Starnes; Joshua Tabor

## Authors

### Ann R. Cannon

**Ann Cannon**is the Watson M. Davis Professor of Mathematics and Statistics at Cornell College in Mount Vernon, Iowa, where she has taught statistics for 30 years. She earned her MA and PhD in statistics from Iowa State University, and her BA in mathematics from Grinnell College. Ann is a Fellow of the American Statistical Association (ASA) and won the Mu Sigma Rho (national statistics honor society) William D. Warde Statistics Education Award. Ann has been very involved with the Statistics and Data Science Education Section of the ASA, serving on the executive committee as member-at-large, secretary/treasurer, and chair. She has also served on the ASA/MAA Joint Committee on Undergraduate Statistics, and as the Secretary/Treasurer and Chair of the Iowa Chapter of the ASA. Ann is currently associate editor for the

*Journal of Statistics and Data Science Education*. Ann has been involved with the AP® Statistics Reading for 20 years, serving as Reader, Table Leader, Question Leader, and Assistant Chief Reader. Ann is coauthor of

*STAT2: Modeling with Regression and ANOVA*(now in its second edition), a textbook designed for the college statistics course following the introductory statistics course. In her spare time, Ann enjoys playing the French horn (particularly in pit orchestras for musical theater), reading, and traveling.

### Daren S. Starnes

**Daren Starnes**has taught a variety of statistics courses — including Introductory Statistics, AP® Statistics, and Mathematical Statistics — for 25 years. He earned his MA in mathematics from the University of Michigan and his BS in mathematics from the University of North Carolina at Charlotte. Daren has been a Reader, Table Leader, and Question Leader for the AP® Statistics exam for over 20 years. As a College Board consultant since 1999, Daren has led hundreds of workshops for AP® Statistics teachers throughout the United States and overseas. He frequently presents in-person and online sessions about statistics teaching and learning for high school and college faculty. Daren is an active member of the American Statistical Association (ASA), the National Council of Teachers of Mathematics (NCTM), the American Mathematical Association of Two-Year Colleges (AMATYC), and the International Association for Statistical Education (IASE). He served on the ASA/NCTM Joint Committee on the Curriculum in Statistics and Probability for six years. While on the committee, he edited the

*Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: A Pre-K–12 Curriculum Framework*report. Daren is coauthor of The Practice of Statistics (now in its seventh edition), the best-selling textbook for AP® Statistics, and of

*Statistics and Probability with Applications*(now in its fourth edition), a popular choice for high school introductory statistics. Daren and his wife Judy enjoy traveling, rambling walks, jigsaw puzzles, and spending time with their three sons and seven grandchildren.

### Josh Tabor

**Josh Tabor**has enjoyed teaching Introductory and AP® Statistics for more than 26 years. He received a BS in mathematics from Biola University, in La Mirada, California. In recognition of his outstanding work as an educator, Josh was named one of five finalists for Arizona Teacher of the Year in 2011. He is a past member of the AP® Statistics Development Committee (2005–2009), as well as an experienced Table Leader, Question Leader, and Exam Leader at the AP® Statistics Reading. In 2013, Josh was named to the SAT® Mathematics Development Committee. Josh is a member of the American Statistical Association (ASA) and was a reviewer for the ASA’s Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II). Each year, Josh leads many workshops and frequently speaks at local, national, and international conferences. In addition to teaching and speaking, he has authored articles in The American Statistician, The Mathematics Teacher, STATS Magazine, and The Journal of Statistics Education. Josh is coauthor of The Practice of Statistics (now in its seventh edition), the best-selling textbook for AP® Statistics, and of Statistics and Probability with Applications (now in its fourth edition), a popular choice for high school introductory statistics. Combining his love of statistics and sports, Josh teamed with Christine Franklin to write Statistical Reasoning in Sports, an innovative textbook for statistical literacy courses. Outside of work, Josh enjoys gardening, traveling, and playing board games with his family.

**Introductory Statistics: A Student-Centered Approach**

First Edition| 2024

Ann Cannon; Daniel Starnes; Joshua Tabor

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