Final answer:
Abigail may be correct if the scatter plot shows that most data points are near the line, indicating a good line of fit for linear regression, assuming a linear relationship is observed and outliers are not influencing the line significantly.
Step-by-step explanation:
Abigail's assertion regarding the line of fit on a scatter plot depends on how closely the data points align with the drawn line. For there to be a good line of fit, most data points should lie near the line of strong positive or negative association. A good line of fit is typically represented by a least-squares regression line, which minimizes the sum of the squares of the differences between observed and predicted values.
The suitability of a linear regression model can be assessed by examining the scatter plot and calculating the correlation coefficient. If a significant correlation coefficient is present and the data points form a pattern that suggests a linear relationship, then a linear regression is generally appropriate. However, if the scatter plot indicates that a curve would be more suitable, statisticians might choose to use alternative methods, like curve fitting, to model the data.
Furthermore, outliers can have a distinct effect on the line of fit. If present, outliers can skew the results and produce a line that does not accurately represent the pattern of the majority of the data points. It is also important when assessing a scatter plot to visualize whether the line of fit captures the trend without being overly influenced by outliers.