
Box plot review (article) | Khan Academy
The five-number summary is the minimum, first quartile, median, third quartile, and maximum. In a box plot, we draw a box from the first quartile to the third quartile. A vertical line goes through …
Interpreting quartiles (practice) | Khan Academy
The following box plot shows the number of aces Olga served during each tennis match. Olga served fewer than what number of aces during about 75 % of tennis matches?
Box and whisker plot: how to construct (video) | Khan Academy
The box and whiskers plot is summary of our data and often can be used to identify low and high outliers. For instance, to find a low outlier, we can use the equation: Q1 - 1.5 (Q3-Q1).
Reading box plots (also called box and whisker plots) (video)
A quartile is a number that, along with the median, splits the data into quarters, hence the term quartile. One quarter of the data is the 1st quartile or below.
Interpreting box plots (video) | Khan Academy
A box and whisker plot is a handy tool to understand the age distribution of students at a party. It helps us identify the minimum, maximum, median, and quartiles of the data.
Worked example: Creating a box plot (odd number of data points)
A box-and-whisker plot is a handy tool for visualizing data. By ordering numbers, we can find the range, median, and quartiles. Practice makes perfect when mastering these concepts!
Worked example: Creating a box plot (even number of data points)
Candlesticks resemble box plots, but their bodies reflect opening and closing prices, and their wicks show the true high and low prices for a time period. In box plots, the box represents …
Reading box plots (practice) | Khan Academy
Here's a box plot that summarizes the average monthly rainfall of several cities. Find the interquartile range (IQR) of the data.
Creating box plots (practice) | Khan Academy
Worked example: Creating a box plot (even number of data points) Constructing a box plot Creating box plots Reading box plots Reading box plots Interpreting box plots Interpreting …
Identifying outliers with the 1.5xIQR rule - Khan Academy
A commonly used rule says that a data point is an outlier if it is more than 1.5 ⋅ IQR above the third quartile or below the first quartile. Said differently, low outliers are below Q 1 1.5 ⋅ IQR …