To match the correlation coefficients with their scatterplots, let's analyze each scatterplot and then match it to the closest correlation value.

1. Positive vs. Negative Correlation:

   • Scatterplots with a positive slope (points going from bottom left to top right) will have positive correlations.
   • Scatterplots with a negative slope (points going from top left to bottom right) will have negative correlations.

2. Strength of Correlation:

   • Strong correlations (near -1 or 1) will have points closely aligned along a line.
   • Weak correlations (near 0) will show a more dispersed pattern.

3. Scatterplot Analysis:

   • Top left scatterplot: Shows no clear upward or downward trend, points are quite spread out. This suggests a weak correlation close to 0. Likely the negative correlation with the smallest magnitude, r = -0.346.
   • Top right scatterplot: Points are moderately spread but show a slight downward trend, indicating a moderate negative correlation. This could match r = -0.421.
   • Bottom left scatterplot: Shows a moderate upward trend, with points clustered moderately closely along an upward slope. This would likely match a moderate positive correlation, r = 0.702.
   • Bottom right scatterplot: Points are closely aligned in an upward trend, suggesting a very strong positive correlation. This would correspond to r = 0.988.

4. Assignments:

   • Top left: $r = -0.346$
   • Top right: $r = -0.421$
   • Bottom left: $r = 0.702$
   • Bottom right: $r = 0.988$

Would you like further clarification on how these correlations were determined, or additional help with another problem?

Related Questions:

1. What is a correlation coefficient, and how is it interpreted?
2. How can we visually distinguish between weak and strong correlations in scatterplots?
3. Why is it important to understand the direction (positive or negative) of a correlation?
4. How do outliers affect the correlation in a scatterplot?
5. What are the limitations of using correlation to interpret relationships between variables?

Tip: Always look at both the direction and the tightness of data points around a line to judge correlation strength and direction effectively.