Question

_______ involving examining data for values out of the coded range, examining data for values that are extreme, and comparing responses that show inconsistency.

Answer 1

Potential outliers are data points that deviate significantly from the others. They can be identified visually or numerically by comparing them to a threshold. For example, in an exam grade dataset, a value of 740 would be considered an outlier on a scale of zero to 100.

Step-by-step explanation:

A potential outlier is a data point that is significantly different from the other data points. These special data points may be errors or some kind of abnormality, or they may be a key to understanding the data.

We can identify outliers by using different methods. One method is to visually examine a scatter plot and look for data points that fall significantly outside the range of the other data points. Another method is to calculate residuals and compare them to twice the standard deviation. If a data point's residual is greater than this threshold, it can be considered an outlier.

For example, in an exam grade dataset with a scale of zero to 100, a value of 740 would be considered an outlier. The professor may need to investigate further to determine if it was a data entry error or a valid score.