New to This Edition

  • Technology-Specific Learning Aids in MyLab Select MyLab exercises have learning aids (Help Me Solve This & View an Example) that offer technology-specific help for StatCrunch (-SC), the TI-84 graphing calculators (-TI), and Excel (-E).  Exercises in MyLab with the -SC, -TI, or -E designation will have learning aids that contain technology specific help for your students.  For example, in Section 3.2, Problem 27-SC offers specific StatCrunch tech help; Problem 27-E offers specific Excel tech help; Problem 27-TI offers specific TI-84 tech help.
  • Threaded Health and Nutrition Problems Based on the feedback received from the threaded Tornado problems, the seventh edition now includes a corresponding threaded set of problems based on the National Health and Nutrition Examination Survey (NHANES) data obtained from the Centers for Disease Control and Prevention (CDC). In addition, the author wrote corresponding MyLab problems around this data set.  The problems may serve as a semester-long project for your students.
  • Videos There are many new lecture videos by author Michael Sullivan that have been updated to provide a detailed presentation of the material, improve audio quality, and address compliance concerns.
  • MediaShare is a classroom tool for active, media-based learning. This feature facilitates active learning by making it easy to exchange, read, and respond to instructional content. Choose from a curated library or create your own video quizzes and projects.
  • Dynamic Study Modules use cognitive science to help students study chapter topics through quick practice and personalized remediation. As a result, students can build confidence and deepen their understanding in key topics. Modules specific to Statistics 7/e were written by Heidi Lyne.
  • Soft Skills/Affective Domain Written by George Woodbury. These provide the opportunity for your students to learn about growth mindset and develop study skills as they progress through the course.
  • Technology Step-by-Step Guides The Technology Step-by-Step guides provide instruction on how to obtain statistical results for StatCrunch, the TI-84 graphing calculator and Excel. These are available under Learning Tools in the Video & Resource Library.
  • R Support The Technology Step-by-Step that is included before every end of section exercise set, now includes support for R. There is a complete R guide available at https://sullystats.com/r-guidebook.
  • Simulation & Randomization Sections Simulation and randomization methods are a popular approach to presenting hypothesis testing. The seventh edition makes it easier to incorporate this approach to your course.
    • Sections 10.2A/10.2B cover hypothesis tests for a proportion using simulation with a P-value approach (Section 10.2A) followed by hypothesis tests for a proportion using the normal model with a P-value approach.
    • Sections 10.3A/10.3B cover hypothesis tests for a mean using simulation and the bootstrap with a P-value approach (Section 10.3A) followed by hypothesis tests for a mean using Student’s t-distribution with a P-value approach.
    • Sections 11.1A/11.1B cover hypothesis tests for two independent proportions using randomization methods with a P-value approach (Section 11.1A) followed by hypothesis tests for two independent proportions using the normal model with a P-value approach.
    • Sections 11.2A/11.2B cover hypothesis tests for two dependent means using the bootstrap with a P-value approach (Section 11.2A) followed by hypothesis tests for two dependent means using the Student’s t-distribution with a P-value approach.
    • Sections 11.3A/11.3B cover hypothesis tests for two independent means using randomization methods with a P-value approach (Section 11.3A) followed by hypothesis tests for two independent means using the Student’s t-distribution with a P-value approach.
    • Sections 14.1A/14.1B cover hypothesis tests for the slope of the least-squares regression model using randomization methods with a P-value approach (Section 14.1A) followed by hypothesis tests for the slope of the least-squares regression model using the Student’s t-distribution with a P-value approach.