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Understanding Graphical Representation of Bivariate Data

In this lesson, we explore how to graphically represent bivariate data, enhancing our understanding of relationships between two variables. We will examine scatter plots as a primary tool for visualizing these relationships and discuss the distinctions between correlation and causation. By analyzing real-world examples, such as the relationship between work hours and income or household televisions and life expectancy, students will learn to interpret graphs critically and understand underlying factors influencing the data. Engaging activities will reinforce these concepts.

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Understanding Graphical Representation of Bivariate Data

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  1. Lesson 7 Aim: How can we represent bivariate data graphically?

  2. Aim: How can we represent bivariate data graphically? Problem of the Day

  3. Aim: How can we represent bivariate data graphically? Problem of the Day

  4. Aim: How can we represent bivariate data graphically? Guided Practice

  5. Aim: How can we represent bivariate data graphically? Guided Practice

  6. Aim: How can we represent bivariate data graphically? Guided Practice

  7. Aim: How can we represent bivariate data graphically? Guided Practice

  8. Aim: How can we represent bivariate data graphically? Guided Practice This is an example of a scatter plot. It shows the relationship between two variables (bivariate)

  9. Aim: How can we represent bivariate data graphically? Guided Practice

  10. Aim: How can we represent bivariate data graphically? Guided Practice

  11. Aim: How can we represent bivariate data graphically? This is also an example of causation (if a student works more hours, he or she will make more money)

  12. Aim: How can we represent bivariate data graphically? Guided Practice This is also an example of causation (if a student works more hours, he or she will make more money)

  13. Aim: How can we represent bivariate data graphically? Guided Practice Correlation is not the same as causation!! For example: There is a positive correlation between the number of televisions owned by a household and the average life expectancy of the family members.

  14. Aim: How can we represent bivariate data graphically? Guided Practice Correlation is not the same as causation!! For example: There is a positive correlation between the number of televisions owned by a household and the average life expectancy of the family members. Owning more televisions does not really cause you to live longer. What might explain the relationship?

  15. Aim: How can we represent bivariate data graphically? Guided Practice Correlation is not the same as causation!! For example: There is a positive correlation between the number of televisions owned by a household and the average life expectancy of the family members. Owning more televisions does not really cause you to live longer. What might explain the relationship? Owning more televisions means you have more money, which you also have to spend on healthcare.

  16. Aim: How can we represent bivariate data graphically? Independent Practice

  17. Aim: How can we represent bivariate data graphically? Independent Practice

  18. Aim: How can we represent bivariate data graphically? Classwork Homework p. 33. Examples 1, 2; Quick Check 1, 2 p. 35: ex. 1 GPS p. 36: ex. 16 pp. 748–749: Examples 1, 2, 3; Quick Check 1, 2, 3 p. 750: Examples 1, 2, 6 p. 749–750: Examples 4, 5; Quick Check 4, 5 p. 750: ex. 9, 10 pp. 35–36: ex. 2, 4, 6, 8, 12, 14, 16, 17 Challenge: p. 36: ex. 19, 20 Test Prep: p. 36–37: ex. 21 Mixed Review: p. 37: ex. 26, 27 Chapter Test: p. 50: 12, 19 pp. 750–751: ex. 3, 4, 6, 11, 12, 13–16 Challenge: p. 751: ex. 17 Test Prep: p. 751: ex. 18, 19 Mixed Review: 21, 25, 26, 28

  19. Aim: How can we represent bivariate data graphically? Lesson 6 Quiz

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