Here are the essential concepts you must grasp in order to answer the question correctly.
Sample Correlation Coefficient (r)
The sample correlation coefficient, denoted as r, quantifies the strength and direction of a linear relationship between two variables. It ranges from -1 to 1, where values close to 1 indicate a strong positive correlation, values close to -1 indicate a strong negative correlation, and values around 0 suggest no correlation. Understanding r is crucial for interpreting how changes in one variable may relate to changes in another.
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Strength of Correlation
The strength of correlation refers to how closely the data points cluster around a line of best fit. A higher absolute value of r signifies a stronger correlation, regardless of the sign. For instance, r = 0.918 and r = -0.932 both indicate strong correlations, but the latter is stronger due to its absolute value being closer to -1, demonstrating a more pronounced relationship.
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Positive vs. Negative Correlation
Correlation can be positive or negative, indicating the direction of the relationship between variables. A positive correlation (r > 0) means that as one variable increases, the other also tends to increase, while a negative correlation (r < 0) indicates that as one variable increases, the other tends to decrease. Understanding this distinction is essential for interpreting the implications of the correlation coefficient in real-world contexts.
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