To match the values of the correlation coefficient $r$ with their respective scatterplots, it’s helpful to understand how different $r$ values relate to the strength and direction of a scatterplot’s linear association:

1. $r = -1$: Perfect negative linear relationship. Points lie exactly on a line sloping downward.
2. $r = -0.991$: Very strong negative correlation, close to -1, showing a strong downward trend with points close to a line.
3. $r = -0.715$: Moderate negative correlation. Points trend downward but with more scatter away from a line.
4. $r = -0.336$: Weak negative correlation. A vague downward trend with more scatter, making the linear relationship less obvious.
5. $r = 0.991$: Very strong positive correlation, close to 1, showing a strong upward trend with points close to a line.

When examining the scatterplots:

• Look for the strength of clustering around a line to assess whether $r$ is closer to -1 or 1.
• A weaker trend with more spread would indicate values closer to -0.336 or -0.715.

If you have access to the scatterplots through the icon mentioned, you can apply this knowledge to match each $r$ value. Let me know if you need help with any specific scatterplot details or interpretations!

Would you like to go over the principles of correlation coefficients or how they affect scatterplot shapes?

1. What does a correlation coefficient of zero imply about the scatterplot?
2. How do positive and negative $r$ values affect the slope direction of the trend line?
3. How does the magnitude of $r$ (close to 1 or 0) influence the scatterplot's appearance?
4. What are some potential outliers, and how might they affect the correlation coefficient?
5. Why is it useful to understand both direction and strength in correlation?

Tip: Always examine the scale and range of both axes on a scatterplot; they can sometimes influence the perception of correlation strength.