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Section 4.1

Section 4.1. Exploring Associations between Two Quantitative Variables ?. Example:. Is there, on a national scale, an association between TV watching and obesity? What do you think? What does the data Show?. Data:.

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Section 4.1

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  1. Section 4.1 Exploring Associations between Two Quantitative Variables?

  2. Example: • Is there, on a national scale, an association between TV watching and obesity? • What do you think? • What does the data Show?

  3. Data: The average hours of TV watched by adults in 18 industrialized countries and the Obesity rate of adults in those countries. Does there seem to be a associated? Lets look at a bar graph and see if that helps.

  4. Data:

  5. New Tool: Scatter Plot: • To build a scatter plot treat your explanatory variable as x, and your response variable as y, and plot your data on an (x , y) plane.

  6. New Tool: Scatter Plot: • We look at three things on a scatter Plot: • Is there a linear association? • What is the direction? • How strong is the association?

  7. Correlation. • Correlation is a numerical value that summarizes the direction and strength of the association between two quantitative variables. • Properties: • Denoted r. • Positive r indicated a positive association • Negative r indicated a negative association • Values fall within the interval [-1, 1] • The closer to r = zero the weaker the association.

  8. Correlation Would you expect a positive association, a negative association or no association between the age of the car and the mileage on the odometer? • Positive association • Negative association • No association

  9. New Tool: Scatter Plot: • What would we expect as the r value for the scatter plot below?

  10. Questions

  11. Calculating Correlation.

  12. Remember the TV thing: r = 0.5378

  13. Remember the TV thing: r = 0.0884

  14. Questions

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