Skewness And Kurtosis. A symmetrical distribution will have a skewness of 0. So we can conclude from the above discussions that the horizontal push or pull distortion of a normal distribution curve gets captured by the skewness measure and the vertical push or pull distortion gets captured by the kurtosis measure.
So we can conclude from the above discussions that the horizontal push or pull distortion of a normal distribution curve gets captured by the skewness measure and the vertical push or pull distortion gets captured by the kurtosis measure. Kurtosis is a measure of whether the data are heavy tailed or light tailed relative to a normal distribution. Today we will try to give a brief explanation of these measures and we will show how we can calculate them in r.
So we can conclude from the above discussions that the horizontal push or pull distortion of a normal distribution curve gets captured by the skewness measure and the vertical push or pull distortion gets captured by the kurtosis measure.
Another less common measures are the skewness third moment and the kurtosis fourth moment. Looking at s as representing a distribution the skewness of s is a measure of symmetry while kurtosis is a measure of peakedness of the data in s. Skewness tells you the amount and direction of skew departure from horizontal symmetry and kurtosis tells you how tall and sharp the central peak is relative to a standard bell curve. Kurtosis is a measure of whether the data are heavy tailed or light tailed relative to a normal distribution.
