Chapter : 2

What Are Data Processing ?


The mean is the value obtained by adding all of the values together and dividing by the number of observations.


The median is the point at which the organised series is divided into two equal halves. It is not affected by the actual value.


The most common value in a distribution is called the mode.


The scattering of scores around the measure of central tendency is referred to as dispersion. It’s a metric for determining how much individual items or numerical data fluctuate or spread around an average value.


The gap between the highest and minimum values in a series of distributions is known as a range (R). This simply indicates the distance between the lowest and highest score in a series. It’s also known as the sum of the top and lowest scores.

Quartile Deviation (Q.d)

It measures absolute dispersion slightly better than the range. However, it overlooks the tail observation. The results are almost always sufficiently diverse when we compute the quartile deviations of multiple samples from a population. The term for this is sampling fluctuation. It is not a widely used dispersion metric. The quartile deviation obtained from sample data does not allow us to draw any conclusions about the population’s quartile deviation.

Mean Deviation

The average of the absolute difference between the elements of a data set and the mean (average deviation) or the median element is the absolute deviation for that data set (median absolute deviation). The mean deviation, also known as the average deviation, is the sum of the absolute departures of observations from an appropriate average, such as the arithmetic mean, median, or mode.

Standard Deviation

The most often used metric of dispersion is the standard deviation (SD). The square root of the average of squares of deviations. It’s always measured as a percentage of the mean. The root mean square deviation is the standard deviation.