Examples From:
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JmpIn
Minitab
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Statview

scatterplot from StatView

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JmpIn

scatterplot from Jmp

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Minitab

scatterplot from Minitab

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scatterplot from Excel

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© 1999, Duxbury Press. >r. If the points for the observations appear to be scattered randomly in the scatterplot, then there is no relationship and the correlation coefficient equals zero. When this page loads, the graph below shows an example of a random scatter of points with a correlation of zero.

Shortly we will learn how to calculate the correlation coefficient. For now, use the interactive graph below to see how the scatterplot looks for different values of the correlation coefficient r.

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Move the slider to increase and decrease the relationship between the two variables.

Click on the "Switch Sign" button to jump between positive and negative relationships of the same strength.

 
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If the scores on the two variables tend to go up together, there is a positive relationship between the two variables. In positive relationships, if the score on one of the variables is high, the corresponding score on the other variable tends to be high also. For example, height and weight of people have a positive relationship: taller people generally are heavier than shorter people and heavier people are generally taller than lighter people. When the relationship is positive, the correlation coefficient has a positive sign.

If the scores on the two variables tend to go in opposite directions, there is a negative relationship between the two variables. In negative relationships, if the score on one of the variables is high, the corresponding score on the other variable tends to be low. For example, speed of doing a task and the accuracy with which the task is done have a negative relationship. At high speeds, accuracy tends to be lower than for low speeds and accuracy tends to be higher when speeds are low. When the relationship is negative, the correlation coefficient has a negative sign.

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© 1999, Duxbury Press. e a window of computer examples of this graph.';return true;"