To match each scatterplot with the correct correlation coefficient, let's analyze the general pattern of each scatterplot and the possible values for the correlation:

The given correlations are:

• a. -0.29
• b. 0.53
• c. -0.91
• d. 0.97

Analysis of Scatterplots:

1. First Scatterplot (Top)

   • The data points seem to have a strong positive linear relationship. The points are closely clustered around an upward slope. This indicates a correlation close to +1.
   • Match: d. 0.97

2. Second Scatterplot (Second from Top)

   • The points are loosely scattered with no strong pattern, but there is a slight downward trend. This suggests a weak negative correlation.
   • Match: a. -0.29

3. Third Scatterplot (Second from Bottom)

   • The points are strongly clustered around a negative linear trend, indicating a high negative correlation.
   • Match: c. -0.91

4. Fourth Scatterplot (Bottom)

   • The points display a moderate positive linear relationship, but they are more spread out compared to the first scatterplot. This indicates a moderate positive correlation.
   • Match: b. 0.53

Conclusion:

• First scatterplot: d. 0.97
• Second scatterplot: a. -0.29
• Third scatterplot: c. -0.91
• Fourth scatterplot: b. 0.53

Would you like further details or have any questions?

Related Questions:

1. What is the meaning of correlation coefficient values between -1 and 1?
2. How can you visually determine if a correlation is positive or negative?
3. What are some common real-life examples of positive and negative correlations?
4. What does a correlation coefficient close to 0 signify?
5. How do outliers affect the correlation of a dataset?

Tip:

Correlation does not imply causation. Even if two variables show a strong correlation, it doesn't mean that one causes the other to change.