The average distance between each data value and the mean is the mean absolute deviation (MAD) of a data set. A measure of variance in a data set is the mean absolute deviation. The mean absolute deviation tells us how "scattered" the values in a data set are.
As a result, the mean and mean absolute deviation are the best ways to describe the centre and variance.
The metrics of dispersion provide this information. The three most frequent metrics of dispersion are range, interquartile range, and standard deviation.
While both metrics are based on deviations from the mean(x - \bar{x}), the MAD utilises the absolute values of the deviations, whereas the standard deviation uses the squares. Both strategies produce non-zero differences. Simply put, the MAD is the average of these nonnegative (absolute) variances.
In statistics, the two most essential metrics are variance and standard deviation. Standard deviation is a measure of the distribution of statistical data, whereas variance is a measure of how data points differ from the mean.
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please provide notes also in pdf
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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