• Measures of dispersion or variability are measures of individual differences of the members of the sample o They give indication of how scores in a sample are dispersed or spread around the mean o Provide information about the data that is not available from measures of central tendency

Eg: If we categorize the data points and observe that the category A has 20, B has 23 and C has 32 data points, then the mode is category C. Dispersion: Dispersion is used to measure the variability in the data or to see how spread out the data is. It measures how much the scores in a distribution vary from the typical score.

As an example, suppose the mean of a set of incomes is $60,200, the standard deviation is $5,500, and the distribution of the data values approximates the normal distribution. Then an income of $69,275 is calculated to have a z-score of 1.65.

Data Analysis Examples The pages below contain examples (often hypothetical) illustrating the application of different statistical analysis techniques using different statistical packages. Each page provides a handful of examples of when the analysis might be used along with sample data, an example analysis and an explanation of the output .

In statistics, dispersion is the extent to which a distribution is stretched or squeezed. Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. Dispersion is contrasted with location or central tendency, and together they are the most used properties of …

Jun 11, 2009· In statistics, dispersion has two measure types. The first is the absolute measure, which measures the dispersion in the same statistical unit. The second type is the relative measure of dispersion, which measures the ratio unit. In statistics, there are many techniques that are applied to measure dispersion.

These statistics describe how the data varies or is dispersed (spread out). The two most commonly used measures of dispersion are the range and the standard deviation. Rather than showing how data are similar, they show how data differs (its variation, spread, or dispersion).

This example shows how to compute and compare measures of dispersion for sample data that contains one outlier. Generate sample data that contains one outlier value. x = [ones(1,6),100] x = 1×7 1 1 1 1 1 1 100 Compute the interquartile range, mean absolute deviation, range, and standard deviation of the sample data. . Exploratory Analysis of .

Given a set of N data values, the addition of another data value (to make N + 1 values) always increases the variance and standard deviation of the data set (unless the data value is equal to the mean, in which case these two measures of dispersion remain unchanged). As a result, a sample always has the tendency of underestimating the standard .

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