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Jan
Skewness And Kurtosis Definition. 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. If skewness is between ½ and ½ the distribution is approximately symmetric. The greater the kurtosis the higher the probability of getting extreme values. Next we subtract 3 from the sample kurtosis and get the excess kurtosis.
Approaches to follow when the data is skewed. However the two concepts must not be confused with each other. In statistics skewness and kurtosis are the measures which tell about the shape of the data distribution or simply both are numerical methods to analyze the shape of data set unlike plotting graphs and histograms which are graphical methods. Skewness and Kurtosis in Statistics shape of distributions Skewness and kurtosis are two important measure in statistics. A distribution or data set is said to be symmetric if it looks the same to the left and right points of the center. Skewness and kurtosis are two commonly listed values when you run a softwares descriptive statistics function.
Whereas skewness measures symmetry in a distribution kurtosis measures the heaviness of the tails or the peakedness.
Multi-normality data tests. If skewness is between 1 and ½ or between ½ and 1 the distribution is moderately skewed. These are normality tests to check the irregularity and asymmetry of the distribution. In finance kurtosis is used as a measure of financial risk. Both left and right sides of. 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.