Chapter 2: Displaying and Summarizing Categorical Data

• A pie chart visually represents the proportion of categories in a dataset.
• To calculate the size of each sector, multiply the relative frequency by 360 degrees.
• Use a protractor to accurately measure and draw sectors.
• The area principle ensures that the chart accurately reflects data proportions.

Each section follows the same learning progression: understand the concept, practice the skill, think like a statistician, then apply your learning to authentic data.

Whether learning independently or teaching a class, the learning path below is designed to help students master the concepts in this section. Page references below correspond to the Real World Statistics instructional system.

Step 1: Watch the lesson video

Watch the lesson video and take notes on the key ideas.

Step 2: Read the section

Read pages 30-33 to reinforce the concepts introduced in the lesson.

Step 3: Practice what you've learned

Complete the 2 multi-part practice questions on pages 33-34. Check your work using the Answer Key, then review and correct any questions you missed before moving on.

Step 4: Think like a statistician
Explore Enrichment: Role-Based Statistical Analysis Tasks and the Chapter Dataset to apply statistical thinking in real-world contexts.

If you'd like additional guidance while working through the practice questions, explore the Worked Solutions Manual for step-by-step solutions and explanations.

Examples of authentic statistical work supported by this section include:

• organizing raw categorical responses (e.g., voter polls, school surveys, customer choice tallies) into frequency and relative frequency tables to reveal how a group is distributed 
• converting categorical counts (e.g., streaming preferences from a student poll, service usage logs from a community survey) into proportional displays such as pie charts that show part-to-whole structure 
• interpreting categorical distributions (e.g., transportation choices from commuter data, purchase patterns from retail records) to explain what categories dominate or are least represented in context

You may use these ideas to design your own investigations or explore the Real World Statistics System.

Enrichment: Role-Based Statistical Analysis Tasks place students in the role of a statistician working on real research problems, where they evaluate data, justify decisions, and communicate findings clearly. Each section includes multiple enrichment tasks, including one with a model response that demonstrates statistical reasoning in context, showing how evidence is evaluated, decisions are justified, and conclusions are communicated. This serves as a bridge to working with authentic datasets and independent statistical investigation within each chapter.

Available in the Interactive eBook at the end of the section (page 34) and in the Appendix of the Print Book (page 401).

Teaching with Real World Statistics: These structured learning paths—including Enrichment tasks and Chapter Datasets—can be adapted or copied directly into Canvas, Google Classroom, Schoology, and other learning management systems to simplify lesson planning and student assignments.