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Jul 31, 2023 · In descriptive statistics, a box plot or boxplot (also known as a box and whisker plot) is a type of chart often used in explanatory data analysis. Box plots visually show the distribution of numerical data and skewness by displaying the data quartiles (or percentiles) and averages.
A box plot, sometimes called a box and whisker plot, provides a snapshot of your continuous variable’s distribution. They particularly excel at comparing the distributions of groups within your dataset.
A Box Plot is the visual representation of the statistical five number summary of a given data set. A Five Number Summary includes: Minimum. First Quartile. Median (Second Quartile) Third Quartile. Maximum.
- Visualize the Distribution of Values in a Dataset. Suppose a basketball coach wants to visualize the distribution of points scored by players on his team so he creates the following box plot
- Compare Two or More Distributions. Suppose a sports analyst wants to compare the distribution of points scored by basketball players on three different teams so he creates the following box plots
- Identify Outliers. Suppose a basketball coach wants to know if any of his players are outliers in terms of points scored. He decides to create the following box plot to visualize the distribution of points scored by his players
Jul 17, 2024 · A boxplot, also known as a box plot, box plots, or box-and-whisker plot, is a standardized way of displaying the distribution of a data set based on its five-number summary of data points: the “minimum,” first quartile [Q1], median, third quartile [Q3] and “maximum.”
- Michael Galarnyk
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Sep 21, 2018 · Why Are Boxplots Useful? Boxplots are useful because they help us visualize five important descriptive statistics of a dataset: the minimum, lower quartile, median, upper quartile, and maximum. Boxplots also help us easily answer questions like: What is the median height of the plants?
Box plots are used to show distributions of numeric data values, especially when you want to compare them between multiple groups. They are built to provide high-level information at a glance, offering general information about a group of data’s symmetry, skew, variance, and outliers.
