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In this session, we saw how the SSE can be used as criteria to determine which line best fits a set of data points. The best fit is the line with the smallest SSE. This line is referred to as the least squares line because, for a given set of data points, it is the line that minimizes the sum of the squared errors. In the following Interactive Activity, you will see how these squares can be represented graphically. The least squares line is the line that minimizes the total area of all the squares formed when the vertical distance from the data points to the line is used as the side lengths of the squares. Note 4
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