Interactive Statistics: Informed Decisions Using Data, 3rd edition

Published by Pearson (February 7, 2023) © 2024

  • Michael Sullivan Joliet Junior College
  • George Woodbury College of the Sequoias

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For courses in Introductory Statistics.

Students do statistics actively as they learn

Interactive Statistics: Informed Decisions Using Data, 3rd Edition encourages students to experience statistics in new and dynamic ways as they encounter concepts. Written fully in MyLab® Statistics, it combines text, multimedia and assessment into a seamless learning experience. Interactive Assignments for each chapter section prompt students to “read a little, watch a little, do a little” to develop deeper conceptual connections. This practical approach in an interactive, guided learning environment promotes students' understanding, knowledge retention and ability to connect statistics to the world at large.

Hallmark features of this title

  • Interactive Assignments provide Preparing for This Section review and exercises, and Learning Objectives for This Section preview. They include:
    • Step-by-Step examples to guide students from problem to solution; Excel video solutions (along with StatCrunch, TI-83/84 Plus, and by-hand calculation videos) accompany Step-by-Step examples
    • Embedded simulation and applet activities; Animations at the start of most sections
    • Innovative lightboard videos featuring Mike Sullivan
    • Reading Assessment questions.
  • Chapter Review includes a Chapter Summary video and downloadable MindMap, list of key chapter Vocabulary and Formulas, Chapter Objectives with review exercises, Review Exercises and Chapter Test with complete, worked-out answers, and a Chapter Review Quiz that populates personalized homework.

New and updated features of this title

  • New sections include 14.3, Multiple Regression; C.1, Estimating a Population Standard Deviation; C.2, Hypothesis Tests for a Population Standard Deviation.
  • New Integrated Review chapter explores Functions, Exponential Functions, and Logarithmic Functions.
  • New and updated additional exercises are “paper and pencil” problems that instructors can use to supplement MyLab exercises, and are ideal for formative assessment or group work.
    • These are available in MyLab within each section's Interactive Assignment and are hidden from student view by default.  
  • New and updated student activities include Data Wrangling; Lengths of Tornadoes; Standard Deviation as a Measure of Spread; Tennis Anyone?; Home Run Distances; Fooled by Randomness; Comparing Rental Car Prices; What is a P-Value?; COVID-19, Taxis, and Benford's Law; and many more.

Features of MyLab Statistics for the 3rd Edition

  • New Technology-Specific MyLab exercises include learning aids (Help Me Solve This and View an Example) that include technology specific help for StatCrunch (-SC), TI-83/84 (-TI), and Excel (-E). In these exercises students will now see help that includes screenshots and click path support for the analysis technology they're using.
  • New real data-based algorithmic exercises: Written by Michael Sullivan, these exercises are based on real data sets that utilize the power of MyLab's algorithms to generate unique problems for students.
  • New Threaded Tornado exercises: These problems utilize the same data set that measures a variety of variables among all tornadoes that struck the United States between 1950 and 2020.  They appear throughout the text by utilizing the data set to answer questions relevant to the material presented within the section.   
  • Updated lecture videos: Many are revised to provide a detailed presentation of the material, improve audio quality, and address compliance concerns.  
  • Essay questions (-Ess): These free-response questions, many of which are algorithmic, can be assigned in MyLab in a quiz or exam. They must be graded by the instructor. 

1: Data Collection

  • 1.1 Introduction to the Practice of Statistics
  • 1.2 Observational Studies versus Designed Experiments
  • 1.3 Simple Random Sampling
  • 1.4 Other Effective Sampling Methods
  • 1.5 Bias in Sampling
  • 1.6 The Design of Experiments
  • Chapter 1 Review
  • Chapter 1 Practice Test
  • Chapter 1 Projects

2: Organizing and Summarizing Data

  • Preparing for Section 2.1: Organizing Qualitative Data
  • 2.1 Organizing Qualitative Data
  • Preparing for Section 2.2: Organizing Quantitative Data: The Popular Displays
  • 2.2 Organizing Quantitative Data: The Popular Displays
  • 2.3 Additional Displays of Quantitative Data
  • 2.4 Graphical Misrepresentations of Data
  • Chapter 2 Review
  • Chapter 2 Practice Test
  • Chapter 2 Projects

3: Numerically Summarizing Data

  • Preparing for Section 3.1: Measures of Central Tendency
  • 3.1 Measures of Central Tendency
  • 3.2 Measures of Dispersion
  • Preparing for Section 3.3: Measures of Central Tendency and Dispersion from Grouped Data
  • 3.3 Measures of Central Tendency and Dispersion from Grouped Data
  • 3.4 Measures of Position
  • 3.5 The Five-Number Summary and Boxplots
  • Chapter 3 Review
  • Chapter 3 Practice Test
  • Chapter 3 Projects

4: Describing the Relation between Two Variables

  • Preparing for Section 4.1: Scatter Diagrams and Correlation
  • 4.1 Scatter Diagrams and Correlation
  • Preparing for Section 4.2: Least-Squares Regression
  • 4.2 Least-Squares Regression
  • Preparing for Section 4.3: Diagnostics on the Least-Squares Regression Line
  • 4.3 Diagnostics on the Least-Squares Regression Line
  • Preparing for Section 4.4: Contingency Tables and Association
  • 4.4 Contingency Tables and Association
  • Chapter 4 Review
  • Chapter 4 Practice Test
  • Chapter 4 Projects

5: Probability

  • Preparing for Section 5.1: Probability Rules
  • 5.1 Probability Rules
  • 5.2 The Addition Rule and Complements
  • 5.3 Independence and the Multiplication Rule
  • 5.4 Conditional Probability and the General Multiplication Rule
  • 5.5 Counting Techniques
  • 5.6 Simulation
  • 5.7 Putting It Together: Which Method Do I Use?
  • Chapter 5 Review
  • Chapter 5 Practice Test
  • Chapter 5 Projects

6: Discrete Probability Distributions

  • Preparing for Section 6.1: Discrete Random Variables
  • 6.1 Discrete Random Variables
  • Preparing for Section 6.2: The Binomial Probability Distribution
  • 6.2 The Binomial Probability Distribution
  • 6.3 The Poisson Probability Distribution
  • Chapter 6 Review
  • Chapter 6 Practice Test
  • Chapter 6 Projects

7: The Normal Probability Distribution

  • Preparing for Section 7.1: Properties of the Normal Distribution
  • 7.1 Properties of the Normal Distribution
  • Preparing for Section 7.2: Applications of the Normal Distribution
  • 7.2 Applications of the Normal Distribution
  • Preparing for Section 7.3: Assessing Normality
  • 7.3 Assessing Normality
  • Preparing for Section 7.4: The Normal Approximation to the Binomial Probability Distribution
  • 7.4 The Normal Approximation to the Binomial Probability Distribution
  • Chapter 7 Review
  • Chapter 7 Practice Test
  • Chapter 7 Projects

8: Sampling Distributions

  • Preparing for Section 8.1: Distribution of the Sample Mean
  • 8.1 Distribution of the Sample Mean
  • Preparing for Section 8.2: Distribution of the Sample Proportion
  • 8.2 Distribution of the Sample Proportion
  • Chapter 8 Review
  • Chapter 8 Practice Test
  • Chapter 8 Projects

9: Estimating the Value of a Parameter

  • Preparing for Section 9.1: Estimating a Population Proportion
  • 9.1 Estimating a Population Proportion
  • Preparing for Section 9.2: Estimating a Population Mean
  • 9.2 Estimating a Population Mean
  • 9.3 Putting It Together: Which Procedure Do I Use?
  • 9.4 Estimating with Bootstrapping
  • Chapter 9 Review
  • Chapter 9 Practice Test
  • Chapter 9 Projects

10: Hypothesis Tests Regarding a Parameter

  • Preparing for Section 10.1: Estimating a Population Mean
  • 10.1 The Language of Hypothesis Testing
  • Preparing for Section 10.2 Hypothesis Tests for a Population Proportion
  • 10.2 Hypothesis Tests for a Population Proportion
  • Preparing for Section 10.3 Hypothesis Tests for a Population Mean
  • 10.3 Hypothesis Tests for a Population Mean
  • Preparing for Section 10.3A Hypothesis Tests on a Population Mean Using Simulation and the Bootstrap
  • 10.3A Hypothesis Tests on a Population Mean Using Simulation and the Bootstrap
  • Chapter 10 Review
  • Chapter 10 Practice Test
  • Chapter 10 Projects

11: Inference on Two Samples

  • Preparing for Section 11.1: Inference about Two Population Proportions
  • 11.1 Inference about Two Population Proportions: Independent Samples
  • 11.1A Using Randomization Techniques to Compare Two Proportions
  • Preparing for Section 11.2: Inference about Two Population Means: Dependent Samples
  • 11.2 Inference about Two Population Means: Dependent Samples
  • Preparing for Section 11.2A: Using Bootstrapping to Conduct Inference on Two Dependent Means
  • 11.2A Using Bootstrapping to Conduct Inference on Two Dependent Means
  • Preparing for Section 11.3: Inference about Two Population Means: Independent Samples
  • 11.3 Inference about Two Population Means: Independent Samples
  • 11.3A Using Randomization Techniques to Compare Two Independent Means
  • 11.4 Putting It Together: Which Procedure Do I Use?
  • Chapter 11 Review
  • Chapter 11 Practice Test
  • Chapter 11 Projects

12: Inference on Categorical Data

  • Preparing for Section 12.1: Goodness-of-Fit Test
  • 12.1 Goodness-of-Fit Test
  • Preparing for Section 12.2: Tests for Independence and the Homogeneity of Proportions
  • 12.2 Tests for Independence and the Homogeneity of Proportions
  • Preparing for Section 12.3: Inference about Two Population Proportions: Dependent Samples
  • 12.3 Inference about Two Population Proportions: Dependent Samples
  • Chapter 12 Review
  • Chapter 12 Practice Test
  • Chapter 12 Projects

13: Comparing Three or More Means

  • Preparing for Section 13.1: Comparing Three or More Means: One-Way Analysis of Variance
  • 13.1 Comparing Three or More Means: One-Way Analysis of Variance
  • Preparing for Section 13.2: Post Hoc Tests on One-Way Analysis of Variance
  • 13.2 Post Hoc Tests on One-Way Analysis of Variance
  • Chapter 13 Review
  • Chapter 13 Practice Test
  • Chapter 13 Projects

14: Inference on the Least-Squares Regression Model and Multiple Regression

  • Preparing for Section 14.1: Testing the Significance of the Least-Squares Regression Model
  • 14.1 Testing the Significance of the Least-Squares Regression Model
  • 14.1A Using Randomization Techniques on the Slope of the Least-Squares Regression Line
  • Preparing for Section 14.2: Confidence and Prediction Intervals
  • 14.2 Confidence and Prediction Intervals
  • Preparing for Section 14.3: Introduction to Multiple Regression
  • Chapter 14 Review
  • Chapter 14 Practice Test
  • Chapter 14 Projects
Appendix

About our authors

Michael Sullivan, III is an adjunct professor of Mathematics at Florida SouthWestern State College. He took this position after retiring from Joliet Junior College (the nation’s oldest public community college). He taught at Joliet Junior College for 25 years. Prior to teaching at Joliet Junior College, Michael taught high school mathematics. He holds graduate degrees from DePaul University in both mathematics and economics.

Michael has been actively presenting at both national and regional conferences for over 25 years. He currently has a Statistics series in its 7th edition and a Developmental Mathematics series in its 4th edition. He also writes a Precalculus series with his father.

Michael’s interests in the classroom lie in developing exciting and innovative techniques for delivering course content to help keep his students engaged. He strongly believes in developing students’ communication skills (both written and oral) and developing students' conceptual understanding of mathematics. His experience in course redesign, corequisites, and writing texts for the college-level math and statistics courses gives him a unique insight into what students need to be successful.

George Woodbury earned a bachelor's degree in Mathematics from the University of California - Santa Barbara and a master's degree in Mathematics from California State University - Northridge. He currently teaches at College of the Sequoias in Visalia, CA, just outside of Fresno. George has been honored as an instructor by both his students and his colleagues. Aside from teaching and writing, George served as the department chair of the math/engineering division from 1999 through 2004. He has been a user of MyLab Math and MyLab Statistics since inception, continually coming up with creative ways to integrate his teaching methods with technology. He actively blogs his thoughts on math, statistics, teaching and study skills. 

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