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Creating Scatter Plots and Understanding the Relation Between Two Variables

Creating Scatter Plots and Understanding the Relation Between Two Variables. Assist. Prof. E. Çiğdem Kaspar, PhD. 74. 72. 70. Height. 68. 66. 64. 62. 60. 58. 4. 6. 8. 10. 12. 14. Foot Length. Suppose we wished to graph the relationship between foot length . and height.

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Creating Scatter Plots and Understanding the Relation Between Two Variables

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  1. Creating Scatter Plots and Understanding the Relation Between Two Variables Assist. Prof. E. Çiğdem Kaspar, PhD.

  2. 74 72 70 Height 68 66 64 62 60 58 4 6 8 10 12 14 Foot Length Suppose we wished to graph the relationship between foot length and height of 20 subjects. In order to create the graph, which is called a scatterplot or scattergram, we need the foot length and height for each of our subjects.

  3. 1. Find 12 inches on the x-axis. 2. Find 70 inches on the y-axis. 3. Locate the intersection of 12 and 70. Assume our first subject had a 12 inch foot and was 70 inches tall. 4. Place a dot at the intersection of 12 and 70. Height Foot Length

  4. 5. Find 8 inches on the x-axis. 6. Find 62 inches on the y-axis. Assume that our second subject had an 8 inch foot and was 62 inches tall. 7. Locate the intersection of 8 and 62. 8. Place a dot at the intersection of 8 and 62. 9. Continue to plot points for each pair of scores.

  5. Notice how the scores cluster to form a pattern. The more closely they cluster to a line that is drawn through them, the stronger the linear relationship between the two variables is (in this case foot length and height).

  6. If the points on the scatterplot have an upward movement from left to right, we say the relationship between the variables is positive. If the points on the scatterplot have a downward movement from left to right, we say the relationship between the variables is negative.

  7. A positive relationship means that high scores on one variable are associated with high scores on the other variable are associated with low scores on the other variable. It also indicates that low scores on one variable

  8. A negative relationship means that high scores on one variable are associated with low scores on the other variable. are associated with high scores on the other variable. It also indicates that low scores on one variable

  9. Correlation simply measures the degree to which the two vary together. A positive correlationindicates that as the values of onevariable increase the values of the other variable increase, whereas a negativecorrelationindicates that as the valuesof one variable increase the values of the other variable decrease.

  10. Not only do relationships have direction (positive and negative), they also have strength (from 0.00 to 1.00 and from 0.00 to –1.00). The more closely the points cluster toward a straight line, the stronger the relationship is. r = 0.00 r = 0.10 r = 0.20 r = 0.30 r = 0.40 r = 0.50 r = 0.60 r = 0.70 r = 0.80 r = 0.90 r = 1.00

  11. A set of scores with r= –0.60 has the same strength as a set of scores with r= 0.60 because both sets cluster similarly.

  12. NO Pearson r Linear Relationship Curvilinear Relationship For this unit, we use Pearson’s r. This statistical procedure can only be used when BOTH variables are measured on a continuous scale and you wish to measure a linear relationship.

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