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In statistics, measures of dispersion refer to positive real numbers that help to measure the variability of data about a central point. What are the 5 Measures of Dispersion? The absolute measures of dispersion are variance, standard deviation, mean deviation, quartile deviation, and range.
Measures of Dispersion are used to represent the scattering of data. Learn about the measure of dispersion in statistics, including its types, formulas, and examples in this article.
In statistics, the measures of dispersion help to interpret the variability of data i.e. to know how much homogenous or heterogeneous the data is. In simple terms, it shows how squeezed or scattered the variable is.
When we analyze a dataset, we often care about two things: 1. Where the “center” value is located. We often measure the “center” using the mean and median. 2. How “spread out” the values are. We measure “spread” using range, interquartile range, variance, and standard deviation.
Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered. On the other hand, when the variance is small, the data in the set is clustered.
Statistical Dispersion Explained: Why It Matters in Everyday Decisions. In statistics, measures of dispersion, or variability, provide insights into how spread out or clustered a dataset is. Statistical dispersion complements measures of central tendency (like mean, median, and mode) by comprehensively understanding the data's distribution.
Recognize, describe, calculate, and analyze the measures of the spread of data: variance, standard deviation, and range.