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Univariate data
Data consisting of observations of a single variable for each individual or item (for example, one height measurement per student).
Distribution
The overall pattern of a data set, including its center, spread, and shape (for example, symmetry, skewness, clusters, and gaps).
Before calculating anything, identify the type of data, because it affects which graphs and measures are meaningful.
Categorical data
Data grouped into categories or labels (for example, eye color, type of transport). Arithmetic operations like averaging are not meaningful.
Numerical data
Data measured or counted as numbers (for example, height, time, number of siblings). Numerical data can be summarized using measures such as mean and quartiles.
Numerical data is often divided into:
Most of the univariate techniques in this topic (stem-and-leaf diagrams, quartiles, five-number summary, box plots, outliers) are designed for numerical data.
Stem-and-leaf diagram
A display that organizes numerical data by separating each value into a stem and a leaf, allowing the original data values to be reconstructed.
To describe a "typical" or "central" value, the most common measures are the mean and the median.
Mean
The arithmetic average of a data set, found by adding all values and dividing by the number of values.
Median
The middle value when the data set is ordered. If there are two middle values, the median is their average.
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