Lesson 8: 1.8 Summarizing Quantitative Data: Boxplots and Outliers
Duration of Days: 3
Lesson Objective
Use the 1.5× IQR rule to identify outliers.
Make and interpret boxplots of quantitative data.
Compare distributions of quantitative data with boxplots.
How do outliers affect the shape, center, and spread of the distribution?
How can we use the boxplot to visualize the shape, center, and spread of the distribution?
How can we compare the distributions of two or more data sets using boxplots?
Outlier
Five Number Summary
Boxplot
HSS.ID.A.3
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
Students will be able to use the 1.5×IQR rule to identify outliers in a data set.
Students will be able to create and interpret boxplots to visualize the distribution of quantitative data.
Students will be able to compare the distributions of two or more data sets using boxplots.
NBA Legend? (Example pg 72)
Using the median for the 1.5 X IQR rule insteasd of Q1 and Q3.
For advanced students: Introduce more complex data sets and challenge them to analyze the data in greater depth.
For struggling students: Provide additional support and simplified data sets.
Teacher assigns examples from the textbook and other resources.
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