IM 8.6.10 Lesson: Using Data Displays to Find Associations

Here is a two-way table that gives some results from the survey. Complete the table, assuming that all students answered both questions.

To the nearest percentage point, what percentage of students who play a sport [i]don’t[/i] play a musical instrument?

To the nearest percentage point, what percentage of students who [i]don’t[/i] play a sport also [i]don’t[/i] play a musical instrument?

Your teacher will give you a two-way table about the number of people in your class who play sports or musical instruments. Complete this table to make a two-way table for the data from earlier. The table will show relative frequencies by row.

Make a segmented bar graph for the table. Use one bar of the graph for each row of the table.

Complete the table to make a two-way table for the data from earlier. The table will show relative frequencies by column.

Using the values in the table, make a segmented bar graph. Use one bar of the graph for each column of the table.

Based on the two-way tables and segmented bar graphs, do you think there is an association between playing a sport and playing a musical instrument? Explain how you know.

[size=150]An eraser factory has five machines. One machine makes the eraser shapes. Then each shape goes through the red machine, blue machine, yellow machine, or green machine to have a side colored.[br][br]The manager notices that an uncolored side of some erasers is flawed at the end of the process and wants to know which machine needs to be fixed: the shape machine or some of the color machines. The manager collected data on the number of flawed and unflawed erasers of each color.[/size][br][br][img]data:image/png;base64,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[/img][br]Work with a partner. Each of you should make one segmented bar graph for the data in the table. One segmented bar graph should have a bar for each [i]row [/i]of the table. The other segmented bar graph should have one bar for each [i]column [/i]of the table.

Are the flawed erasers associated with certain colors? If so, which colors? Explain your reasoning.

Based on the federal budgets for 2009, the table shows where some of the federal money was expected to go. The values are in billions of U.S. Dollars.

[img]data:image/png;base64,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[/img][br][br]Why would a segmented bar graph be more useful than the table of data to see any associations between the country and where the money is spent?

Create a segmented bar graph that represents the data from the table.

Is there an association between the country’s budget and their spending in these areas? Explain your reasoning.