Box Plot Outlier

Using the information below, identify which of the following is NOT an outlier:
Q1 = 53
Q2 = 55
Q3=61

• 73

• 40

• 80

• 73.5

Use the 1.5 IQR rule to determine if there are any outliers for the following numbers:
84, 88, 72, 74, 98, 16, 94

• Yes, 16 is an outlier.

• Yes, 33 is an outlier.

• Yes, 98 is an outlier.

• There are no numbers less than 39 or greater than 124, therefore there are no outliers.

Using the information below, identify which of the following is an outlier:
Q1 = 3
Q2 = 7
Q3 = 12

• 22

• 25.5

• -8.5

• 26

When using IQR to find outliers, which formula will identify the lower limit?

• 1.5(IQR)

• Q1 + 1.5(IQR)

• Q1 - 1.5(IQR)

• Q3 + 1.5(IQR)

In a box plot, what does a point that lies outside the "whiskers" represent?

• Median

• Mean

• Outlier

• Mode

Which of the following is true about outliers in a box plot?

• They are always above the upper quartile

• They are always below the lower quartile

• They can be either above the upper quartile or below the lower quartile

• They are always at the median

Consider a dataset of final exam scores for a large introductory statistics course. The instructor, Maya, uses a boxplot to visualize the distribution of scores. Interpret the outliers in this boxplot and explain their significance.

• Outliers in a boxplot are always errors in the data collection process.

• Outliers in a boxplot are data points that fall below the lower whisker or above the upper whisker, typically defined as 1.5 times the interquartile range away from the first and third quartiles. They are represented as individual points outside the main box of the plot, indicating students whose scores are significantly different from the rest.

• Outliers have no impact on the interpretation of the data.

• Outliers are always indicative of a mistake in the statistical analysis.

How is the upper boundary for outliers calculated in a box plot?

• Upper Quartile + (1.5 * IQR)

• Upper Quartile - (1.5 * IQR)

• Lower Quartile + (1.5 * IQR)

• Lower Quartile - (1.5 * IQR)

How does the presence of an outlier affect a boxplot?

• It has no effect

• It shifts the median

• It affects the shape and spread of the data

• It changes the range

How can the presence of outliers affect the interpretation of a box plot?

• It can make the median appear higher than it actually is

• It can distort the perceived spread of the data

• It can change the position of the first and third quartiles

• It has no effect on the interpretation