In assessing whose line of fit best represents the dataset, it's important to consider the concept of a line of best fit, which aims to minimize the distance (or error) between the line and the data points.
Let's analyze the options provided:
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A. Edgar's line because his line passes through more data points.: This could be a reason, but a line of fit is not necessarily determined solely by the number of points it passes through. A better line of fit may not pass through as many data points if it better represents the overall trend.
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B. Edgar's line because his line passes through the first and last point in the dataset.: While passing through the first and last points may seem convincing, it doesn't guarantee that the line accurately represents the overall trend of the data.
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C. Molly's line because there is approximately the same number of points above and below the line.: This is a strong argument for a good line of fit, as it indicates that the line balances the dataset well, minimizing the overall error.
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D. Molly's line because her line passes through fewer data points.: Similar to option A, the number of points a line passes through does not solely determine its effectiveness as a line of fit.
Based on these considerations, C is the best option. Molly's line is preferable because it maintains an equal distribution of points above and below it, suggesting it provides a more accurate representation of the overall trend in the dataset.