the use of the pearson's of skewness
if coefficient of skewness is zero then distribution is symmetric or zero skewed.
distinguish between dispersion and skewness
Skewness is measured as the third standardised moment of the random variable. Skewness is the expected value of {[X - E(X)]/sd(X)}3 where sd(X) = sqrt(Variance of X)
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.
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.
Answer this question...similarities and differences between normal curve and skewness
Skewness is measured as the third standardised moment of the random variable. Skewness is the expected value of {[X - E(X)]/sd(X)}3 where sd(X) = sqrt(Variance of X)
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.
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.
Skewness is not a characteristic.