Outliers and Box Plot Analysis

The video tutorial explains how to identify outliers in a dataset using the 1.5 interquartile range (IQR) rule and how to modify a standard box plot to represent these outliers. It begins with an introduction to outliers and box plots, followed by a detailed explanation of calculating lower and upper outliers using the IQR. The tutorial also demonstrates how to modify the box plot by replacing outliers with asterisks and adjusting the whiskers to reflect the new data range.

What is the primary purpose of a modified box plot?

• To calculate the standard deviation

• To show the mode of the data set

• To identify and highlight outliers

• To display the mean of the data set

Which of the following is a characteristic of an outlier?

• It is the mode of the data set

• It is the median value

• It is outside the typical range of data

• It is within the interquartile range

Which of the following is NOT part of the five-number summary?

• Maximum

• Mean

• Median

• Minimum

What is the 1.5 IQR rule used for?

• Calculating the mean

• Determining outliers

• Finding the median

• Calculating the range

How is the interquartile range (IQR) calculated?

• Quartile 1 minus Quartile 3

• Maximum minus Minimum

• Quartile 3 minus Quartile 1

• Mean minus Median

What is the lower boundary for outliers if Quartile 1 is 13.5 and the IQR is 6.75?

• 20.25

• 6.75

• 13.5

• 0

What symbol is used to replace an outlier in a modified box plot?

• A circle

• A triangle

• An asterisk

• A square

What is the new end of the whisker for lower values after identifying an outlier?

• 13.5

• 6.75

• 3

• 12

What is the first step in modifying a box plot to account for outliers?

• Calculate the mean

• Identify the outliers

• Replace outliers with an asterisk

• Extend the whiskers

How do you determine if a value is a lower outlier?

• If it is less than Quartile 1

• If it is less than the lower boundary

• If it is greater than Quartile 3

• If it is greater than the upper boundary