Interquartile Range and Outliers

This video tutorial explains how to find the interquartile range (IQR) and identify outliers in a dataset. It covers the concept of quartiles, percentiles, and the method to calculate IQR. The video includes two examples demonstrating the step-by-step process of arranging data, finding quartiles, calculating IQR, and determining outliers using the IQR method.

What is the median of a data set also known as?

• Third Quartile

• Second Quartile

• First Quartile

• Fourth Quartile

Tags

CCSS.6.SP.B.5C

Which quartile represents the 75th percentile?

• Q3

• Q1

• Q4

• Q2

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CCSS.6.SP.B.5C

What is the first step in calculating the IQR?

• Calculate the mean

• Find the maximum value

• Arrange the numbers in ascending order

• Identify the median of the entire data set

Tags

CCSS.6.SP.B.5C

How do you find the interquartile range (IQR)?

• Subtract Q1 from Q3

• Divide Q3 by Q1

• Multiply Q1 by Q3

• Add Q1 and Q3

What is the formula to determine if a number is an outlier?

• Q1 + 1.5 * IQR and Q3 - 1.5 * IQR

• Q1 - 1.5 * IQR and Q3 + 1.5 * IQR

• Q1 / 1.5 + IQR and Q3 * 1.5 - IQR

• Q1 * 1.5 + IQR and Q3 / 1.5 - IQR

Tags

CCSS.6.SP.B.5C

In the first example, what is the IQR of the data set?

• 9

• 6

• 15

• 11

In the first example, is 26 an outlier?

• Yes

• No

• Maybe

• Not enough information

Tags

CCSS.6.SP.B.5C

In the second example, what is the IQR of the data set?

• 15

• 22

• 30

• 54

In the second example, is 54 an outlier?

• Not enough information

• Maybe

• No

• Yes

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CCSS.6.NS.B.3

What is the middle number between 29 and 31 in the second example?

• 31.5

• 28

• 30

• 29.5

Tags

CCSS.6.NS.B.3