“Now You Can Do It” Feature
In addition to many successful, unique First Edition features (i.e. “Developing Your Statistical Sense,” “What Result Would You Expect?” and “What Do These Numbers Mean?”), the Second Edition includes a new feature, “Now You Can Do It.” Found in the margin next to most examples, this learning tool cues students to try related Practicing the Techniques skill-building exercises. When working a particular exercise, students can also easily look back through the section to find the callout to a related example.
Updated Examples and Exercises
Approximately 30% of the examples and exercises are new or updated to represent contemporary topics that will capture students’ interest and show them how statistics applies to many real-life experiences. For example:
New Examples:
• 3.6: Celebrity followers on Twitter
• 5.20: Use of cell-phone apps
New Exercises:
• 7.1.49-51: 2011 Earthquake in Japan
• 8.1.59; 8.2.59; 8.4.41: Wii Game
“Bringing It All Together” Exercises These exercises require students to draw on what they have learned throughout a particular section. A related set of Applying the Concepts exercises are used to tie together the main concepts and techniques learned.
New Hypothesis Testing Notation
Matching the style used in Larose’s Discovering Statistics: Brief Version, The Second Edition uses the most commonly encountered notation ( “equals to”) considered to be easier for students to understand.
New CrunchIT! Coverage
A favorite among instructors, the “Step-by-Step Technology Guides”, which provide instructions for using Minitab, Excel, and TI, now include complete coverage of CrunchIT!, W.H. Freeman’s computational software running on the underlying statistical software R, CrunchIT! is provided at no additional cost with Larose. It can be also accessed from StatsPortal and the interactive eBook.
Additional Topic Coverage
The new edition includes additional topics to allow for its use across a broader spectrum of courses. Some of the new topics include Poisson Probability Distribution, Inference for Two Population Standard Deviations, Randomized Block Design, Two-Way ANOVA, and Multiple Regression.