The least-squares line to summarize the relationship between x and y for the dataset that was used to construct the scatterplot 2 makes sense since the points are roughly scattered along the points (55, 55) steeply up and right to approximately (70, 90), and then steeply down and right to approximately (85, 60), an approximate relationship between x and y can established using linear square regression line because there exists a linear trend in the data.

The dataset used to construct the scatterplot 1 does not fit into a least-square line because the points are roughly scattered along the points (55, 60) in the y-axis and (95, 85) in the x-axis and there do not exists any linear trend among the points. Thus, if a least-square line is used to summarize the relationship between x and y it may not provide a best fit for the data.

Thus, it makes sense to use the least squares line to summarize the relationship between x and y for the data set in the scatterplot 2, but not for the data set in the scatterplot 1.

The dataset used to construct the scatterplot 1 does not fit into a least-square line because the points are roughly scattered along the points (55, 60) in the y-axis and (95, 85) in the x-axis and there do not exists any linear trend among the points. Thus, if a least-square line is used to summarize the relationship between x and y it may not provide a best fit for the data.

Thus, it makes sense to use the least squares line to summarize the relationship between x and y for the data set in the scatterplot 2, but not for the data set in the scatterplot 1.