Representing Outliers in a Boxplot Using Quartiles

This video tutorial explains how to represent outliers in a boxplot by understanding quartiles and the interquartile range (IQR). It covers the five-number summary of a boxplot, how to calculate outliers using quartiles and IQR, and provides a practical example using student data. The tutorial also demonstrates how to adjust a boxplot to show outliers and interpret the differences between boxplots with and without outliers.

What is the five-number summary in a boxplot?

• Mean, median, mode, range, standard deviation

• Minimum, first quartile, median, third quartile, maximum

• First quartile, second quartile, third quartile, mean, maximum

• Minimum, mean, median, mode, maximum

How do you calculate the interquartile range?

• Maximum minus Minimum

• Median minus Mean

• Quartile 3 minus Quartile 1

• Quartile 1 minus Quartile 3

What formula is used to find outliers below the first quartile?

• Quartile 3 minus 1.5 times interquartile range

• Quartile 1 minus 1.5 times interquartile range

• Quartile 1 plus 1.5 times interquartile range

• Quartile 3 plus 1.5 times interquartile range

In the example of students' social media usage, what were the outliers below the median?

• 15, 19, and 22

• 6, 15.5, and 19

• 0, 0, and 3

• 22, 30, and 53

What is the new lower endpoint of the whisker after adjusting for outliers in the example?

• 0

• 6

• 15.5

• 3

How does a boxplot with outliers differ from a basic boxplot?

• It highlights outliers and provides a more detailed view of data distribution.

• It does not include any data points outside the whiskers.

• It only shows the median and ignores other quartiles.

• It shows a symmetric distribution without any tails.

What does the presence of outliers in a boxplot indicate?

• A perfectly symmetric distribution

• A need to recalculate the median

• Data points that are significantly different from the rest

• An error in data collection