Let's say we fit a batch of data to a least-squares regression line. Outliners are points with abnormally high residual values.
Finding the line that "fits" the scatter plot of the data points the best is the technique of linear regression. Given the value of the independent variable, this is typically used to forecast the value of the dependent variable.
Data points or observations that deviate greatly from the majority of the data due to their anomalous separation from the remainder are known as outliers. They must be taken into account since they have a significant impact on the regression line's slope.
As a result, the response is (c) Outliers can easily be detected by linear regression.
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