Distribution is a asymptotic version of continuous frequencies plotted against its classes.

- Distribution tells us where the mean is and how the individual observations are scattered around the mean.
- Distribution with a high peak at its center (mean) will suggest that the density of the observations are rising when the we are nearer to the mean.
- The more the density of the observations far from the mean, the more the variance in the data.
- Distribution are more flat with lower peak at center when they have higher variance.
- With high number of observations discrete distributions start to resemble continuous distributions.
- It can be used to compare nature of two or more different data sets.
- Chebyshev’s theorem specifies that there are certain proportion of data which is covered when we create boundaries from mean using its standard deviation.

1 -(1/k^2)

is the method to find how many percent observations will be covered in the range when k standardized deviations are added and subtracted.

The application of distribution study very admirable, many production processes utilize this concept in their quality testing and analyzing the total production quality.

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Informative for students of statistical economics… You can further explain it by comparing the distribution with different statistics…. like Lepto Kurtic….Meso Kurtic….( Concept of Kurtosis can also be included)….. Keep it up Bro… 🙂

Different Statistics means…… Different cases of Variances…..