LESSON: Effects of Outliers in Bivariate Data

In this lesson video from Khan Academy, you will see examples of the impact of removing outliers on a regression line. 

Removing outliers can effect the calculation called "Coefficient of Determination"($$r^{2}$$, which we haven't talked about much in our class yet.

The coefficient of determination is a statistic that measures how well the regression line fits the data. It is calculated by first finding the Correlation Coefficient ($$r$$) and then squaring it. Squaring the correlation coefficient will always result in a positive number. So even data sets with a negative correlation coefficient will have a positive coefficient of determination. A coefficient of determination equal to 1 indicates that the regression line fits the data perfectly.