Assessing the Fit of a Line Using Residuals

The video tutorial explains how to assess the fit of a line on a scatterplot by using residuals. It covers the concept of a line of best fit and how it can be used to make predictions, such as estimating gas mileage based on car weight. The tutorial emphasizes that predictions have a margin of error, which is quantified by residuals—the difference between observed and predicted values. By comparing the total residuals of different lines, one can determine which line provides the best fit. The lesson concludes by highlighting that the line with the smallest total residuals offers the best overall fit.

Why is it important for residuals to be as small as possible?

What conclusion can be drawn when comparing the total residuals of two lines?