Outliers Practice

Front

What is an outlier in a data set?

Back

An outlier is a data point that differs significantly from other observations in a dataset. It can be much higher or lower than the rest of the data.

Front

How do you identify outliers using the interquartile range (IQR)?

Back

To identify outliers using IQR, calculate the first (Q1) and third quartiles (Q3). The IQR is Q3 - Q1. Any data point below Q1 - 1.5*IQR or above Q3 + 1.5*IQR is considered an outlier.

Front

What is the formula for calculating the interquartile range (IQR)?

Back

IQR = Q3 - Q1, where Q1 is the first quartile and Q3 is the third quartile.

Front

What is the first quartile (Q1)?

Back

The first quartile (Q1) is the median of the lower half of a data set, which separates the lowest 25% of the data from the rest.

Front

What is the third quartile (Q3)?

Back

The third quartile (Q3) is the median of the upper half of a data set, which separates the highest 25% of the data from the rest.

Front

How do you find the median of a data set?

Back

To find the median, arrange the data in ascending order and locate the middle value. If there is an even number of observations, the median is the average of the two middle values.

Front

What does the 1.5 IQR rule state?

Back

The 1.5 IQR rule states that any data point that lies more than 1.5 times the IQR above Q3 or below Q1 is considered an outlier.

Front

What is the significance of identifying outliers in data analysis?

Back

Identifying outliers is important because they can skew the results of statistical analyses and may indicate variability in measurement, experimental errors, or novel phenomena.

Front

Given the data set 10, 59, 67, 72, 73, 76, 88, 92, 106, 111, 115, 169, identify the outlier(s).

Back

The outliers are 10 and 169.

Front

In the data set 16, 22, 23, 25, 27, 29, 35, 40, 43, are there any outliers?

Back

There are no outliers.