To match each scatterplot with its calculated correlation, let's analyze the patterns in the plots and compare them to the provided correlation coefficients:

Correlation coefficients:

• $r = -0.923$: Strong negative linear relationship.
• $r = -0.021$: Almost no correlation.
• $r = 0.951$: Strong positive linear relationship.
• $r = 0.777$: Moderate positive linear relationship.

Scatterplots:

1. Scatterplot (a): The points form a tight, upward trend, indicating a strong positive linear relationship. $r = 0.951$.
2. Scatterplot (b): The points form a moderately strong upward trend, less tight than (a). $r = 0.777$.
3. Scatterplot (c): The points form a strong downward trend, indicating a strong negative linear relationship. $r = -0.923$.
4. Scatterplot (d): The points are scattered randomly with no visible trend. $r = -0.021$.

Matching:

• (a) $\to 0.951$
• (b) $\to 0.777$
• (c) $\to -0.923$
• (d) $\to -0.021$

Would you like further explanation of correlations or any details about linear relationships?

Related Questions:

1. How is the strength of a correlation determined from a scatterplot?
2. What are the limitations of using correlation to measure relationships?
3. Can two variables have a strong correlation but not a causal relationship? Explain.
4. What is the difference between positive and negative correlations?
5. How do outliers affect the correlation coefficient?

Tip: A correlation coefficient $r$ close to +1 or -1 indicates a strong linear relationship, while $r$ near 0 indicates little to no linear relationship.