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Definition Of Skewness And Kurtosis. This is a statistical procedure used in reporting the distribution. Skewness and Kurtosis in Statistics shape of distributions Skewness and kurtosis are two important measure in statistics. In probability theory and statistics skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. In finance kurtosis is used as a measure of financial risk Financial Risk Modeling Financial risk modeling is the process of determining how much risk is present in a particular business investment or series of cash flows.
Of shape give a more precise evaluation. With a skewness of 01098 the sample data for student heights are. 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. A symmetrical dataset will have a skewness equal to 0. In probability theory and statistics skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. Like skewness kurtosis describes the shape of a probability distribution and there are different ways of quantifying it for a theoretical distribution and corresponding ways of estimating it from a sample from a population.
Skewness tells you the amount and direction of skewdeparture from horizontal symmetry and kurtosis tells you how tall and sharp the central peak is relative to a standard bell curve.
Like skewness kurtosis describes the shape of a probability distribution and there are different ways of quantifying it for a theoretical distribution and corresponding ways of estimating it from a sample from a population. It helps to understand where the most information is lying and analyze the outliers in a given data. In this video I am going to discuss the definition of the skewness and the kurtosis in probability theory. In the skewness we have discussed positive skewn. Whereas skewness differentiates extreme values in one versus the other tail kurtosis measures extreme values. Unlike skewness which differentiates extreme values between one tail and another kurtosis computes the absolute values in each tail.