Check all that's TRUE about the "best-fit" line? The line is horizontal when the correlation coefficient is 0 . It is used to predict values that are not in the data. It must contains all scattered points. It makes the least distance from every point. The slope is the correlation coefficient.

The line is horizontal when the correlation coefficient is 0, indicating no relationship between the variables, and it can indeed be used to predict values beyond the data range, a practice known as extrapolation. However, it does not need to contain all scattered points; it's more about minimizing the distance between itself and all points, focusing on the overall trend rather than exact matches. An interesting historical tidbit: the concept of the best-fit line traces back to the early 19th century with the work of mathematicians like Carl Friedrich Gauss, who developed the method of least squares. This technique revolutionized how we analyze data, laying the groundwork for statistical modeling that we still use today! For anyone looking to apply the best-fit line beyond basic stats, think of using it in fields like economics for forecasting trends, or in environmental science for predicting climate changes. It helps in making informed decisions based on data trends rather than guessing, which is super helpful in different real-world contexts!