From the images you have uploaded, I can provide guidance on matching the values of $r$ (correlation coefficients) with the scatterplots. Here's how to interpret each $r$ value:

1. $r = -1$: Perfect negative correlation; points form a straight line with a downward slope.
2. $r = -0.997$: Strong negative correlation; points are very close to a straight line with a downward slope but may have slight deviation.
3. $r = -0.361$: Weak negative correlation; points are somewhat scattered but still trend downward.
4. $r = 0.696$: Moderate positive correlation; points form a trend upward, but there is noticeable scatter.
5. $r = 0.997$: Strong positive correlation; points are close to a straight line with an upward slope.

Observing Scatterplots:

• Scatterplot 3 appears to show a nearly perfect straight line trending downward, which corresponds to $r = -1$.
• Scatterplot 1 has a strong negative trend but with slight scatter, corresponding to $r = -0.997$.
• Scatterplot 4 shows a weak negative trend with significant scatter, corresponding to $r = -0.361$.
• Scatterplot 5 shows a strong positive trend, corresponding to $r = 0.997$.
• Scatterplot 2 has a moderate positive trend with noticeable scatter, corresponding to $r = 0.696$.

Assigning $r$ Values:

1. Scatterplot 1: $r = -0.997$
2. Scatterplot 2: $r = 0.696$
3. Scatterplot 3: $r = -1$
4. Scatterplot 4: $r = -0.361$
5. Scatterplot 5: $r = 0.997$

Let me know if you have further questions or need additional explanations!

Relative Questions:

1. What does the magnitude of $r$ indicate about the strength of correlation?
2. How would you describe the difference between $r = -0.361$ and $r = 0.696$?
3. Why is it important to visually confirm a correlation even when the $r$ value is provided?
4. How can outliers affect the calculation of $r$ and the visual scatterplot?
5. What real-life data might exhibit a strong negative or positive correlation like $r = 0.997$ or $r = -1$?

Tip: Always examine scatterplots alongside their correlation coefficient $r$; even high values of $r$ might not always reflect linearity if outliers dominate