Final answer:
Point A on a scatterplot can either strengthen or weaken the correlation between tree age and height, depending on its position. Correlation coefficient and least-squares line calculations are used to understand the line of best fit and significance of correlation.
Step-by-step explanation:
When biologists collect data on the age and height of trees and plot this information on a scatterplot, the point referred to as Point A could either increase or decrease the correlation between the two variables depending on where it lies on the plot.
If the point is in line with the general trend of the other points, it could strengthen the correlation, while a point that is an outlier could weaken it.
To determine the impact of Point A on the correlation, one would need to calculate the correlation coefficient, which measures the strength and direction of the relationship between the two variables.
A significant correlation coefficient indicates that the relationship is not due to chance. Additionally, biologists might calculate a least-squares line to find the best fit for the data, which can help predict the average height of a tree of a certain age.