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Evaluate a scatter plot that would resemble the shape of a cone facing -->

a) Positive correlation
b) Negative correlation
c) No correlation
d) Perfect correlation

1 Answer

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Final answer:

A scatter plot that would resemble the shape of a cone facing positive correlation would have data points that are close to forming an upward sloping line. On the other hand, a scatter plot that would resemble the shape of a cone facing negative correlation would have data points that are close to forming a downward sloping line. A scatter plot that would resemble the shape of a cone facing no correlation would have data points scattered randomly without following any specific pattern or trend.

Step-by-step explanation:

A scatter plot that would resemble the shape of a cone facing positive correlation would have data points that are close to forming an upward sloping line. This means that as one variable increases, the other variable also tends to increase. An example could be the relationship between temperature and ice cream sales, where as the temperature rises, the sales of ice cream also increase.

On the other hand, a scatter plot that would resemble the shape of a cone facing negative correlation would have data points that are close to forming a downward sloping line. This means that as one variable increases, the other variable tends to decrease. An example could be the relationship between studying time and test scores, where as the studying time increases, the test scores tend to decrease.

A scatter plot that would resemble the shape of a cone facing no correlation would have data points scattered randomly without following any specific pattern or trend. This means that there is no relationship between the two variables. An example could be the relationship between shoe size and favorite color, where there is no logical connection between the two variables.

A scatter plot that would resemble the shape of a cone facing perfect correlation would have data points forming a straight line, either upward or downward. This means that there is a strong relationship between the two variables. An example could be the relationship between height and shoe size in growing children, where as the height increases, the shoe size also increases.

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User Steve Bauman
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