Negative skewness means the average (mean) will be less than the median. Positive skewness means the opposite. I'm not sure if any rule holds for the mode.
the use of the pearson's of skewness
if coefficient of skewness is zero then distribution is symmetric or zero skewed.
Skewness is a measure of the extent to which the probability distribution of a random variable lies more to one side of the mean, as opposed to it being exactly symmetrical.If μ and s are the mean and standard deviation of a random variable X, thenSkew(X) = Expected value of [(X - μ)/s]3
distinguish between dispersion and skewness
The word skewness means the measure of a random variable, which can be positive, negative or undefined. Quite often you may hear that someone has "skewed the numbers".
Negative skewness means the average (mean) will be less than the median. Positive skewness means the opposite. I'm not sure if any rule holds for the mode.
A positive skewness is when the value of mean is greater than the mode. that is, the curve is more skewed at the right hand side or the right tail is longer than the left tail. The negative skewness is when the mean is smaller than the mode, and in this case the curve is more skewed on the left hand side.
The skewness of a random variable X is the third standardised moment of the distribution. If the mean of the distribution is m and the standard deviation is s, then the skewness, g1 = E[{(X - m)/s}3] where E is the expected value. Skewness is a measure of the degree to which data tend to be on one side of the mean or the other. A skewness of zero indicates symmetry. Positive skewness indicates there are more values that are below the mean but the the ones that are above the mean, although fewer, are substantially bigger. Negative skewness is defined analogously.
the use of the pearson's of skewness
if coefficient of skewness is zero then distribution is symmetric or zero skewed.
Skewness is a measure of the extent to which the probability distribution of a random variable lies more to one side of the mean, as opposed to it being exactly symmetrical.If μ and s are the mean and standard deviation of a random variable X, thenSkew(X) = Expected value of [(X - μ)/s]3
distinguish between dispersion and skewness
No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.
skewness=(mean-mode)/standard deviation
describe the properties of the standard deviation.
When the data are skewed to the right the measure of skewness will be positive.