Discover the best method to visually examine numerical variables and identify outliers in data sets using box plots. Learn how box plots effectively summarize data distribution and detect unusual observations for accurate data analysis.
Table of Contents
Question
A market research firm has data sets based on surveys. A data analyst wants to know if any outliers are present in a data set. Which of the following would be the BEST method to examine the numerical variables in the data set visually and find any outliers?
A. Plot the linear correlations between each pair of variables and look for unusual relationships
B. Create a bar chart for each variable and look for any distributions that are unusual.
C. Build a scatter plot of each variable and look for observations that are out of place.
D. Order each variable in a spreadsheet from lowest to highest and look for unusual numbers at the beginning or at the end of the list.
Answer
C. Build a scatter plot of each variable and look for observations that are out of place.
Explanation
To visually examine numerical variables in a data set and identify outliers, the best method is to create a box plot (also known as a box-and-whisker plot) for each variable. Box plots display the distribution of data based on five-number summary: minimum, first quartile, median, third quartile, and maximum. Outliers are plotted as individual points outside the whiskers, making them easy to spot.
Box plots effectively summarize the central tendency, dispersion, and skewness of the data, allowing quick identification of unusual observations that fall outside the normal range. They provide a clear visual representation of the data’s spread and help detect potential outliers without making assumptions about the underlying distribution.
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