Final answer:
To interpret a scatter plot, one must analyze the pattern and calculate the correlation coefficient. High or low correlation must be determined, but it's also essential to recognize that correlation does not imply causation. A curved pattern in the scatter plot might require fitting a curve instead of applying linear regression.
Step-by-step explanation:
To properly interpret a scatter plot and decide whether the X and Y variables are good candidates for linear regression, one should analyze the pattern of the scatter plot and calculate the correlation coefficient. When observing a scatter plot, if the data points form a pattern that suggests a line could be the best fit, linear regression could be appropriate. However, the correlation coefficient also needs to be significant to validate the strength of the linear relationship.
Notably, correlation does not imply causation. So even if there is a high correlation between the two variables, it does not mean that one variable causes the changes in the other variable. Therefore, the correct choice for interpreting the statement regarding correlation and causation would be 'High correlation; may or may not cause' or 'Low correlation; may or may not cause' depending on the calculated correlation coefficient.
If the scatter plot indicates a curved pattern, even with a significant correlation coefficient, a statistician would prefer to fit a curve rather than using linear regression. It is always essential to look at the scatter plot along with calculating the correlation coefficient when deciding on the appropriateness of a linear model.