# Using NBA Statistics for Box and Whisker Plots

This lesson plan uses the heights of NBA players from one professional team as the data for students to create a box and whisker plot. The assessment and extension sections provide questions that require the student to analyze the data and compare it to data when one or more extremes are removed from the data set. Excellent resource for box and whisker plots. This plan could be used as a lesson or for assessment, either formative or summative. Also, data from the sports team in the school's locale could be used instead of the data given in the lesson.

### Standards & Objectives

CCSS.Math.Content.6.SP.A.3
Recognize that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation...
CCSS.Math.Content.6.SP.B.4
Display numerical data in plots on a number line, including dot plots, histograms, and box plots.
CCSS.Math.Content.6.SP.B.5
Summarize numerical data sets in relation to their context, such as by:
CCSS.Math.Content.7.SP.A.2
Use data from a random sample to draw inferences about a population with an unknown characteristic of interest. Generate multiple samples (or...
CCSS.Math.Content.7.SP.B.3
Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the...
CCSS.Math.Content.7.SP.B.4
Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two...
CCSS.Math.Content.8.EE.B.5
Graph proportional relationships, interpreting the unit rate as the slope of the graph. Compare two different proportional relationships...
CCSS.Math.Content.8.EE.B.6
Use similar triangles to explain why the slope m is the same between any two distinct points on a non-vertical line in the coordinate plane; derive...
CCSS.Math.Content.8.F.A.2
Compare properties of two functions each represented in a different way (algebraically, graphically, numerically in tables, or by verbal...
CCSS.Math.Content.8.SP.A.1
Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe...
CCSS.Math.Content.8.SP.A.3
Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For...
GLE 0606.3.5
Use multiple representations including symbolic algebra to model and/or solve contextual problems that involve linear relationships.
GLE 0606.5.2
Interpret representations of data from surveys and polls, and describe sample bias and how data representations can be misleading.
GLE 0701.7.4
Apply and adapt the principles of written composition to create coherent media productions.
GLE 0706.3.2
Understand and compare various representations of relations and functions.
GLE 0706.3.5
Understand and graph proportional relationships.
GLE 0706.3.6
Conceptualize the meanings of slope using various interpretations, representations, and contexts.
GLE 0706.5.1
Collect, organize, and analyze both single- and two-variable data.
GLE 0706.5.2
Select, create, and use appropriate graphical representations of data.
GLE 0706.5.3
Formulate questions and design studies to collect data about a characteristic shared by two populations, or different characteristics within one population.
GLE 0706.5.4
Use descriptive statistics to summarize and compare data.
GLE 0806.3.4
Translate among verbal, tabular, graphical and algebraic representations of linear functions.
GLE 0806.5.2
Select, create, and use appropriate graphical representations of data (including scatterplots with lines of best fit) to make and test conjectures.
SPI 0606.1.1
Make conjectures and predictions based on data.
SPI 0606.5.3
Determine whether or not a sample is biased.
SPI 0706.3.4
Interpret the slope of a line as a unit rate given the graph of a proportional relationship.
SPI 0706.3.5
Represent proportional relationships with equations, tables and graphs.
SPI 0706.5.2
Select suitable graph types (such as bar graphs, histograms, line graphs, circle graphs, box-and-whisker plots, and stem-and-leaf plots) and use them to create...
SPI 0706.5.3
Calculate and interpret the mean, median, upper-quartile, lower-quartile, and interquartile range of a set of data.
SPI 0806.3.4
Translate between various representations of a linear function.
SPI 0806.5.3
Generalize the relationship between two sets of data using scatterplots and lines of best fit.
TSS.Math.6.SP.A.3
Recognize that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation describes how...
TSS.Math.6.SP.B.4
Display a single set of numerical data using dot plots (line plots), box plots, pie charts and stem plots.
TSS.Math.6.SP.B.5
Summarize numerical data sets in relation to their context.
TSS.Math.7.SP.A.2
Use data from a random sample to draw inferences about a population with an unknown characteristic of interest. Generate multiple samples (or simulated...
TSS.Math.7.SP.B.3
Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers...
TSS.Math.7.SP.B.4
Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations.
TSS.Math.8.EE.B.5
Graph proportional relationships, interpreting the unit rate as the slope of the graph. Compare two different proportional relationships represented in...
TSS.Math.8.EE.B.6
Use similar triangles to explain why the slope m is the same between any two distinct points on a non-vertical line in the coordinate plane; know and derive...
TSS.Math.8.F.A.2
Compare properties of two functions each represented in a different way (algebraically, graphically, numerically in tables, or by verbal descriptions).
TSS.Math.8.SP.A.1
Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such...
TSS.Math.8.SP.A.3
Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a...

Alignment of this item to academic standards is based on recommendations from content creators, resource curators, and visitors to this website. It is the responsibility of each educator to verify that the materials are appropriate for your content area, aligned to current academic standards, and will be beneficial to your specific students.

Learning objectives:

In this lesson, students will:

• Collect data on the height of the Houston Rockets' players.
• Create box and whisker plots.
• Compare and analyze different box and whisker plots.
Essential and guiding questions:
• What happened to the medians? Explain why.
• What happened to the maximums? Explain why.
• What happened to the first and third quartiles? Explain why.
• What happened to the mean? Explain why.
• How does the plot change if the shortest player is removed?
• Suppose the height of a player near the middle of the ordered list is removed instead of Yao Ming. How will the statistics change?
• What effect does Yao Ming have on the range and the mode?
• Suppose the heights of Yao Ming and just 4 other numbered players are used to make a box and whisker plot. What effect does removing Yao Ming from the data have on the plot?

### Lesson Variations

Blooms taxonomy level:
Applying
Extension suggestions:
• Students could use another team’s roster and eliminate the tallest or shortest player as suggested in the Assessment Options section.
• Ask students to use the Rockets data again to make a new plot, but this time eliminate player(s) with the median height. What differences do they observe between the plots?
• If Internet access is available, students could research to determine the shortest player in the NBA, and then find that player’s roster.