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Statistics: Informed Decisions Using Data, 6th edition

Published by Pearson (July 23, 2020) © 2021

  • Michael Sullivan Joliet Junior College

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For 1- or 2-semester courses in Introductory Statistics.

Tools to see the bigger picture and make informed choices

Packed with ideas and strategies that work in today's classroom, Statistics: Informed Decisions Using Data embodies the teaching experience of Mike Sullivan III. His practical emphasis shows that statistics is connected not only to concepts, but to the world at large.

The 6th Edition ensures that all features follow the Guidelines for Assessment and Instruction in Statistics Education (GAISE) for the introductory course. It continues to evolve with the goal of providing clear, readable explanations, while challenging students to learn how to think conceptually about statistics.

Hallmark features of this title

  • Putting It Together connects concepts from different chapters to show statistics as a whole, rather than a series of disconnected procedures.
  • Making an Informed Decision chapter openers pose a question and present the statistical concept needed for decision-making.
  • Case Studies conclude each chapter to help students apply their knowledge.
  • Preparing for this Section quizzes verify that students have the knowledge needed for the next section, with page numbers for quick reference.
  • Retain Your Knowledge problems help students recall skills learned earlier in the course.
  • Big Data Problems, marked with an icon, let students analyze data sets with more than 50 observations covering tens of thousands of observations with thousands of variables.

New and updated features of this title

  • Additional larger, more complex data sets support modern data analysis.
  • New Threaded Tornado Problems throughout present a single large data set that measures variables on all tornadoes that struck the United States in 2017. Students learn techniques while using the same data set, showing how data can be manipulated to accomplish various tasks.
  • Over 350 new and updated exercises: Many focus more on explaining results than on computation, and require students to understand pitfalls in faulty statistical analysis. Multiple types of exercises (Vocabulary and Skill Building, Applying the Concepts, Explaining the Concepts) at the end of sections and chapters progress in difficulty level.
  • Over 100 new and updated examples include Step-by-Step annotated examples that guide students in 3 steps: Problem, Approach, Solution. Solutions demonstrate both manual and technology methods where applicable.
  • Updated video program: new lightboard videos featuring the author develop statistical concepts. New and revised animated videos explain concepts or tie material learned earlier in the course with the upcoming chapter or section.
  • New author-specific applets can be used in the classroom, asking students to manipulate data using technology.

Features of MyLab Statistics for the 6th Edition

  • Updated Integrated Review offers corequisite support or can be used to get underprepared students up to speed. It providesembedded, personalized review of prerequisite topics within relevant chapters. All assignments are premade and editable for instructors to assign.
  • Skills Check assignments check each student's understanding of skills required in the following chapter. For any identified skills gaps, a personalized review homework is populated.
  • Videos, a full Integrated Review eText, and new Integrated Review worksheets are available to help students understand the objectives they missed on the Skills Check quiz. The Integrated Review eText includes review from Sullivan's developmental math series.
  • New Personal Inventory Assessments promote self-reflection and engagement, and include topics such as a Stress Management Assessment and Time Management Assessment.
  • New and updated MyLab problems written by the author utilize real data that is resampled from a larger data set. New, assignable applet exercises encourage students to explore statistical concepts.
  • StatCrunch Projects enable students to explore data beyond the classroom. In each, students analyze a large data set in StatCrunch and answer corresponding assignable questions for immediate feedback.
  • Resources for Success
  • Applications Index

I. GETTING THE INFORMATION YOU NEED

  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 Test
    • Making an Informed Decision: What College Should I Attend?
    • Case Study: Chrysalises for Cash

II. DESCRIPTIVE STATISTICS

  1. Organizing and Summarizing Data
    • 2.1 Organizing Qualitative Data
    • 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 Test
    • Making an Informed Decision: Tables or Graphs?
    • Case Study: The Day the Sky Roared
  2. Numerically Summarizing Data
    • 3.1 Measures of Central Tendency
    • 3.2 Measures of Dispersion
    • 3.3 Measures of Central Tendency and Dispersion from Grouped Data
    • 3.4 Measures of Position and Outliers
    • 3.5 The Five-Number Summary and Boxplots
    • Chapter 3 Review
    • Chapter Test
    • Making an Informed Decision: What Car Should I Buy?
    • Case Study: Who Was "A Mourner"?
  3. Describing the Relation between Two Variables
    • 4.1 Scatter Diagrams and Correlation
    • 4.2 Least-Squares Regression
    • 4.3 Diagnostics on the Least-Squares Regression Line
    • 4.4 Contingency Tables and Association
    • 4.5 Nonlinear Regression: Transformations (online)
    • Chapter 4 Review
    • Chapter Test
    • Making an Informed Decision: Relationships among Variables on a World Scale
    • Case Study: Thomas Malthus, Population, and Subsistence

III. PROBABILITY AND PROBABILITY DISTRIBUTIONS

  1. Probability
    • 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 Simulating Probability Experiments
    • 5.7 Putting It Together: Which Method Do I Use?
    • 5.8 Bayes's Rule (online)
    • Chapter 5 Review
    • Chapter Test
    • Making an Informed Decision: The Effects of Drinking and Driving
    • Case Study: The Case of the Body in the Bag
  2. Discrete Probability Distributions
    • 6.1 Discrete Random Variables
    • 6.2 The Binomial Probability Distribution
    • 6.3 The Poisson Probability Distribution
    • 6.4 The Hypergeometric Probability Distribution (online)
    • 6.5 Combining Random Variables (online)
    • Chapter 6 Review
    • Chapter Test
    • Making an Informed Decision: Should We Convict?
    • Case Study: The Voyage of the St. Andrew
  3. The Normal Probability Distribution
    • 7.1 Properties of the Normal Distribution
    • 7.2 Applications of the Normal Distribution
    • 7.3 Assessing Normality
    • 7.4 The Normal Approximation to the Binomial Probability Distribution
    • Chapter 7 Review
    • Chapter Test
    • Making an Informed Decision: Stock Picking
    • Case Study: A Tale of Blood Chemistry

IV. INFERENCE: FROM SAMPLES TO POPULATION

  1. Sampling Distributions
    • 8.1 Distribution of the Sample Mean
    • 8.2 Distribution of the Sample Proportion
    • Chapter 8 Review
    • Chapter Test
    • Making an Informed Decision: How Much Time Do You Spend in a Day . . . ?
    • Case Study: Sampling Distribution of the Median
  2. Estimating the Value of a Parameter
    • 9.1 Estimating a Population Proportion
    • 9.2 Estimating a Population Mean
    • 9.3 Estimating a Population Standard Deviation
    • 9.4 Putting It Together: Which Method Do I Use?
    • 9.5 Estimating with Bootstrapping
    • Chapter 9 Review
    • Chapter Test
    • Making an Informed Decision: How Much Should I Spend for this House?
    • Case Study: Fire-Safe Cigarettes
  3. Hypothesis Tests Regarding a Parameter
    • 10.1 The Language of Hypothesis Testing
    • 10.2 Hypothesis Tests for a Population Proportion
    • 10.3 Hypothesis Tests for a Population Mean
    • 10.4 Hypothesis Tests for a Population Standard Deviation
    • 10.5 Putting It Together: Which Method Do I Use?
    • 10.6 The Probability of a Type II Error and the Power of the Test
    • 10.2A Using Simulation to Perform Hypothesis Tests on a Population Proportion (online)
    • 10.2B Hypothesis Tests for a Population Proportion Using the Normal Model (online)
    • 10.3A Using Simulation and the Bootstrap to Perform Hypothesis Tests on a Population Mean (online)
    • Chapter 10 Review
    • Chapter Test
    • Making an Informed Decision: Selecting a Mutual Fund
    • Case Study: How Old Is Stonehenge?
  4. Inference on Two Population Parameters
    • 11.1 Inference about Two Population Proportions
    • 11.2 Inference about Two Means: Dependent Samples
    • 11.3 Inference about Two Means: Independent Samples
    • 11.4 Inference about Two Population Standard Deviations
    • 11.5 Putting It Together: Which Method Do I Use?
    • 11.1A Using Randomization Techniques to Compare Two Proportions (online)
    • 11.2A Using Bootstrapping to Conduct Inference on Two Dependent Means (online)
    • 11.3A Using Randomization Techniques to Compare Two Independent Means (online)
    • Chapter 11 Review
    • Chapter Test
    • Making an Informed Decision: Which Car Should I Buy?
    • Case Study: Control in the Design of an Experiment
  5. Inference on Categorical Data
    • 12.1 Goodness-of-Fit Test
    • 12.2 Tests for Independence and the Homogeneity of Proportions
    • 12.3 Inference about Two Population Proportions: Dependent Samples
    • Chapter 12 Review
    • Chapter Test
    • Making an Informed Decision: Benefits of College
    • Case Study: Feeling Lucky? Well, Are You?
  6. Comparing Three or More Means
    • 13.1 Comparing Three or More Means (One-Way Analysis of Variance)
    • 13.2 Post Hoc Tests on One-Way Analysis of Variance
    • 13.3 The Randomized Complete Block Design
    • 13.4 Two-Way Analysis of Variance
    • Chapter 13 Review
    • Chapter Test 687
    • Making an Informed Decision: Where Should I Invest?
    • Case Study: Hat Size and Intelligence
  7. Inference on the Least-Squares Regression Model and Multiple Regression
    • 14.1 Testing the Significance of the Least-Squares Regression Model
    • 14.2 Confidence and Prediction Intervals
    • 14.3 Introduction to Multiple Regression
    • 14.4 Interaction and Dummy Variables
    • 14.5 Polynomial Regression
    • 14.6 Building a Regression Model
    • 14.1A Using Randomization Techniques on the Slope of the Least-Squares Regression Line (online)
    • Chapter 14 Review
    • Chapter Test
    • Making an Informed Decision: Buying a Home
    • Case Study: Housing Boom
  8. Nonparametric Statistics
    • 15.1 An Overview of Nonparametric Statistics
    • 15.2 Runs Test for Randomness
    • 15.3 Inference about Measures of Central Tendency
    • 15.4 Inference about the Difference between Two Medians: Dependent Samples
    • 15.5 Inference about the Difference between Two Medians: Independent Samples
    • 15.6 Spearman's Rank-Correlation Test
    • 15.7 Kruskal - Wallis Test
    • Chapter 15 Review
    • Chapter Test
    • Making an Informed Decision: Where Should I Live?
    • Case Study: Evaluating Alabama's 1891 House Bill 504

    Photo Credits

    Appendix A: Tables

    Appendix B: Lines (online)

    Answers

    Index

About our author

With training in mathematics, statistics and economics, Mike Sullivan, III has a varied teaching background that includes 15 years of instruction in both high school- and college-level mathematics. He is currently a full-time professor of mathematics and statistics at Joliet Junior College. Mike has numerous textbooks in publication in addition to his Introductory Statistics Series, including a Developmental Math series and a Precalculus series which he writes with his father, Michael Sullivan.  

Mike built this book in the classroom using feedback from his students. He is well aware of the challenges of students taking an introductory statistics course. His goal is for students to be more informed interpreters of data, so they will become better decision makers with stronger critical-thinking skills. When not in the classroom or writing, Mike enjoys spending time with his children Michael, Kevin and Marissa, and playing golf.

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