What is indicated by a correlation coefficient under 1.0 with dots that are spread apart?

What is indicated by a correlation coefficient under 1.0 with dots that are spread apart?

• A. Strong positive correlation
• B. Weak correlation
• C. Perfect correlation
• D. No correlation

Answer: (B)

A correlation coefficient under 1.0 suggests that there is a relationship between the two variables being analyzed, but the degree of that relationship can vary significantly based on the coefficient's value. When the data points associated with this coefficient are scattered widely apart, it often indicates a weak correlation. In a weak correlation, the data points do not cluster closely along a defined line, which would suggest a strong relationship. Instead, they are spread out, demonstrating that as one variable increases or decreases, the other variable does not consistently follow in a predictable manner. Thus, while there may be some association, the connection is not strong enough to indicate that one variable reliably predicts the other. In contrast, a strong positive correlation would be characterized by data points that closely follow an upward trend with little scatter, whereas a perfect correlation would imply that the data points fall precisely on a straight line. No correlation, on the other hand, would show random scatter without any observable pattern or relationship between the two variables. Therefore, the choice indicating a weak correlation accurately describes the scenario of a correlation coefficient under 1.0 with widely spread dots.

A correlation coefficient under 1.0 suggests that there is a relationship between the two variables being analyzed, but the degree of that relationship can vary significantly based on the coefficient's value. When the data points associated with this coefficient are scattered widely apart, it often indicates a weak correlation.

In a weak correlation, the data points do not cluster closely along a defined line, which would suggest a strong relationship. Instead, they are spread out, demonstrating that as one variable increases or decreases, the other variable does not consistently follow in a predictable manner. Thus, while there may be some association, the connection is not strong enough to indicate that one variable reliably predicts the other.

In contrast, a strong positive correlation would be characterized by data points that closely follow an upward trend with little scatter, whereas a perfect correlation would imply that the data points fall precisely on a straight line. No correlation, on the other hand, would show random scatter without any observable pattern or relationship between the two variables. Therefore, the choice indicating a weak correlation accurately describes the scenario of a correlation coefficient under 1.0 with widely spread dots.