Displaying Quantitative Data: Dotplots (Lesson 1.3)

Chapter 1 - Day 3

Chapter 1
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Day 8
Day 9
Day 10
Day 11
Day 12
Day 13
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Learning Targets
  • Make and interpret dotplots of quantitative data.
  • Describe the shape of a distribution.
  • Describe or compare distributions of quantitative data with dotplots.
Activity: How Many Hours a Week Do You Work?

Experience First

In this activity, students will need to identify the average number of hours they work per week. If you have a class with very few students that have jobs, the question of the day can easily be adjusted, but be sure to use one that also has data from the Census at School website (travel time to school, hours of sleep on a school-night, number of weekly hours hanging out with friends).


Students will collect and graph class data first using this applet. Then they will use the Census at School website to select a random sample of 50 high school students from the state. You might want to show students the website and model how to select a random sample and download the data before they get started on the activity. Students will need a laptop with spreadsheet capabilities.


Be sure to save this data, as you will need it again for Lesson 1.6.

Formalize Later

For any dotplot used in class, it is important to point to a single dot and ask “What does this dot represent?” For this activity, the dot represents “the number of weekly hours worked for one student.” The important idea here is that each dot represents a different student and for each student the dot tells us the number of weekly hours worked. Later in the course, this question will become critical in helping students to understand sampling distributions (and inference!) when each dot will represent a sample.


For question #4, don’t expect students to know exactly what should be discussed when comparing distributions. During the debrief, make them aware that when they are comparing two distributions they should be sure to address Shape, Center, Variability, and Outliers. Some teachers use the acronym VSCO to help students remember (if you don’t know what this is, ask your students).


For now, center can simply be what students think is a “typical” or “middle” value, often where there is a large clump of data. Later in Lesson 1.6, we will introduce mean and median as ways to measure center.


For now, variability can simply be a statement of “the number of hours worked goes from 0 to 25 hours”. Later in Lesson 1.7, we will introduce range, interquartile range, and standard deviation as ways to measure variability.


For now, outliers can be defined loosely as values that seem to be way too big or way too small. Later in Lesson 1.8, we will use a mathematical rule for determining outliers. 

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