Based on the information and scatterplot provided in your image, here's how to evaluate the appropriateness of computing the correlation for the two variables (Amazon price and B&N price):

1. Quantitative Variables Condition:
   Yes, the condition appears to be satisfied because both Amazon price and B&N price are quantitative variables.

2. Straight Enough Condition:
   Yes, the condition appears to be satisfied because the scatterplot indicates a fairly linear relationship between the variables. The points seem to form a linear pattern without obvious curvature.

3. Outlier Condition:
   No, it appears that the condition is not satisfied. The scatterplot shows a potential outlier, as one point is noticeably distant from the main cluster of points.

Therefore, while it is possible to compute the correlation, caution should be taken due to the presence of a potential outlier, as it could affect the correlation value.

Let me know if you’d like detailed calculations or further insights!

Here are 5 related questions:

1. How does an outlier impact the correlation coefficient in this dataset?
2. Could transforming the data (e.g., logarithmic) improve the linearity of the scatterplot?
3. How would removing the outlier influence the computed correlation?
4. What statistical test can be used to confirm the significance of the correlation coefficient?
5. Are there other assumptions besides linearity and outliers that should be checked before calculating correlation?

Tip: Always visualize the data before calculating the correlation coefficient to identify potential issues like outliers or non-linearity!