Lesson 10 - ET - Outlier of Data Set

What is an outlier in a dataset?

• A data point that significantly differs from other data points in the dataset.

• A data point that is always accurate and reliable.

• A data point that is perfectly aligned with other data points in the dataset.

• A data point that is irrelevant to the dataset.

How do you calculate the lower quartile of a dataset?

• Find the mean of the entire dataset.

• Select the highest value in the dataset.

• Calculate the mode of the dataset.

• Arrange data in ascending order, find the median of the lower half of the data.

Tags

CCSS.6.SP.B.5C

Explain the process of calculating the upper quartile.

• The upper quartile is the median of the upper half of the data set.

• The upper quartile is the highest value in the data set.

• The upper quartile is the mode of the data set.

• The upper quartile is the sum of all data points divided by 4.

Tags

CCSS.6.SP.B.5C

What is the interquartile range and how is it calculated?

• Interquartile range = Q3 + Q1

• Interquartile range = Q3 / Q1

• Interquartile range = Q3 - Q1

• Interquartile range = Q3 * Q1

Tags

CCSS.6.SP.B.5C

Describe the IQR method for determining outliers in a dataset.

• The IQR method involves calculating the interquartile range (IQR) of the dataset and identifying outliers as data points falling below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR.

• The IQR method involves calculating the median of the dataset and identifying outliers as data points falling below the median.

• The IQR method involves calculating the range of the dataset and identifying outliers as data points falling outside the range.

• The IQR method involves calculating the mean of the dataset and identifying outliers as data points falling below 2 standard deviations from the mean.

What is the lower fence in the context of outliers?

• Q1 + 2 * IQR

• Q1 - 2 * IQR

• Q1 - 1.5 * IQR

• Q1 + 1.5 * IQR

Define the upper fence when dealing with outliers.

• Q3 - 1.5 * IQR

• Q1 + 1.5 * IQR

• Q1 - 1.5 * IQR

• Q3 + 1.5 * IQR

When should outliers be kept in a dataset and when should they be removed?

• Outliers should always be kept in a dataset to maintain data integrity.

• Outliers should be removed only if they are the majority of the data points.

• Outliers should be kept in a dataset to skew the results in a desired direction.

• Outliers should be kept in a dataset when they represent valid data points or when they provide valuable insights. Outliers should be removed when they are due to errors or anomalies that do not reflect the true nature of the data.

Calculate the lower quartile of the following dataset: 10, 15, 20, 25, 30, 35, 40.

• 25

• 10

• 15

• 30

Tags

CCSS.6.SP.B.5C

Determine if there are any outliers in the dataset: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60.

• Outliers are at 20 and 40

• No outliers

• Outliers are at 15 and 55

• There is one outlier