7.
Explanation
Absolute measures of dispersion provide information about the spread or variability of data points in a dataset without regard to the direction (positive or negative) from a central value. These measures give a sense of how much individual data points deviate from the central tendency.
Range: The range is the simplest measure of dispersion. It is calculated as the difference between the maximum and minimum values in a dataset. A larger range indicates greater variability, while a smaller range suggests less variability.
Mean Deviation (Mean Absolute Deviation): Mean deviation measures the average absolute difference between each data point and the mean (average) of the dataset. It provides an overall sense of how much data values deviate from the mean.
Variance: Variance is a measure of the average squared deviation of each data point from the mean. It considers not only the absolute differences but also the magnitude of those differences. A larger variance indicates more variability, and a smaller variance suggests less variability. It is calculated as the average of the squared differences from the mean.
Standard Deviation: The standard deviation is the square root of the variance. It measures the typical or average absolute distance of data points from the mean. A smaller standard deviation indicates less variability, and a larger standard deviation suggests more variability.
Quartile Deviation (Semi-Interquartile Range): The quartile deviation is half of the interquartile range. It measures the spread of data within the middle 50% of the dataset, which is less affected by outliers. It is calculated as half of the difference between the upper quartile (Q3) and the lower quartile (Q1).
Absolute Range: The absolute range is the difference between the absolute values of the maximum and minimum values in a dataset. This measure considers only the magnitude of differences, ignoring their direction. It can be particularly useful when the data involves quantities that don't have a natural zero point.These measures of dispersion provide valuable information about the variability and spread of data, which is essential for understanding and analysing datasets. The choice of which measure to use depends on the specific characteristics of the data and the objectives of the analysis.