Introductory Statistics: A Student-Centered Approach
First Edition ©2024 Ann Cannon; Daren Starnes; Josh Tabor Formats: Achieve, E-book, Print
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Authors
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Ann 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.
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Daren Starnes
Daren Starnes has taught a variety of statistics courses—including AP® Statistics, Introductory 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 National Council of Teachers of Mathematics (NCTM), the American Statistical Association (ASA), 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) pre-K–12 report. Daren is also coauthor of the popular on-level text Statistics and Probability with Applications (now in its fifth edition) and of the new college text Introductory Statistics: A Student-Centered Approach. Daren and his wife Judy enjoy traveling, rambling walks, jigsaw puzzles, and spending time with their three sons and seven grandchildren.
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Josh Tabor
Josh Tabor has enjoyed teaching on-level and AP® Statistics to high school students for more than 26 years, most recently at The Potter’s School. 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 the 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 Reader, Table Leader, Question Leader, and Exam Leader at the AP® Statistics Reading since 1999. In 2013, Josh was named to the SAT® Mathematics Development Committee. Each year, Josh leads one-week AP® Summer Institutes and one-day College Board workshops around the country and frequently speaks at local, national, and international conferences. In addition to teaching and speaking, Josh has authored articles in The American Statistician, The Mathematics Teacher, STATS Magazine, and The Journal of Statistics Education. Combining his love of statistics and love of sports, Josh teamed with Christine Franklin to write Statistical Reasoning in Sports, an innovative textbook for on-level statistics courses. Josh is also coauthor of the popular on-level text - Statistics and Probability with Applications (now in its fifth edition) and the new college text Introductory Statistics: A Student-Centered Approach. Outside of work, Josh enjoys gardening, traveling, and playing board games with his family.
Table of Contents
Section 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 13.2 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
Product Updates
Authors
-
Ann 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 Starnes
Daren Starnes has taught a variety of statistics courses—including AP® Statistics, Introductory 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 National Council of Teachers of Mathematics (NCTM), the American Statistical Association (ASA), 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) pre-K–12 report. Daren is also coauthor of the popular on-level text Statistics and Probability with Applications (now in its fifth edition) and of the new college text Introductory Statistics: A Student-Centered Approach. 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 on-level and AP® Statistics to high school students for more than 26 years, most recently at The Potter’s School. 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 the 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 Reader, Table Leader, Question Leader, and Exam Leader at the AP® Statistics Reading since 1999. In 2013, Josh was named to the SAT® Mathematics Development Committee. Each year, Josh leads one-week AP® Summer Institutes and one-day College Board workshops around the country and frequently speaks at local, national, and international conferences. In addition to teaching and speaking, Josh has authored articles in The American Statistician, The Mathematics Teacher, STATS Magazine, and The Journal of Statistics Education. Combining his love of statistics and love of sports, Josh teamed with Christine Franklin to write Statistical Reasoning in Sports, an innovative textbook for on-level statistics courses. Josh is also coauthor of the popular on-level text - Statistics and Probability with Applications (now in its fifth edition) and the new college text Introductory Statistics: A Student-Centered Approach. Outside of work, Josh enjoys gardening, traveling, and playing board games with his family.
Table of Contents
Section 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 13.2 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
Product Updates
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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Sometimes also referred to as a spiral-bound or binder-ready textbook, loose-leaf textbooks are available to purchase. This three-hole punched, unbound version of the book costs less than a hardcover or paperback book.
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Introductory Statistics: A Student-Centered Approach
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.
These materials are owned by Macmillan Learning or its licensors and are protected by United States copyright law. They are being provided solely for evaluation purposes only by instructors who are considering adopting Macmillan Learning's textbooks or online products for use by students in their courses. These materials may not be copied, distributed, sold, shared, posted online, or used, in print or electronic format, except in the limited circumstances set forth in the Macmillan Learning Terms of Use and any other reproduction or distribution is illegal. These materials may not be made publicly available under any circumstances. All other rights reserved. © 2020 Macmillan Learning.
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