A measure of skewness is Pearson's Coefficient of Skew. It is defined as: Pearson's Coefficient = 3(mean - median)/ standard deviation The coefficient is positive when the median is less than the mean and in that case the tail of the distribution is skewed to the right (notionally the positive section of a cartesian frame). When the median is more than the mean, the cofficient is negative and the tail of the distribution is skewed in the left direction i.e. it is longer on the left side than on the right.
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if coefficient of skewness is zero then distribution is symmetric or zero skewed.
The coefficient is 7 and the variable is x
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)
A variable is a part of a term which can change. A coefficient is a numerical constant, associated with a variable. For example, in the term 3x^2 , 3 is the coefficient, while x is a variable.
The numerical value that comes before the variable or, if none, the coefficient is 1.The numerical value that comes before the variable or, if none, the coefficient is 1.The numerical value that comes before the variable or, if none, the coefficient is 1.The numerical value that comes before the variable or, if none, the coefficient is 1.