Question

is skewness and kurtosis is independent of charge of origin and scale​

Answer 1

Yes, this is true that skewness and kurtosis is independent of charge of origin and scale

• Skewness:-Depending on the model, skewness may break model assumptions or lower the interpretation of feature relevance if the values of a particular independent variable (feature) are skewed.

• In statistics, skewness is the degree of asymmetry in a probability distribution that differs from the symmetrical normal distribution (bell curve) in a particular collection of data. Knowing a skewness is made easier by using the normal distribution. When we discuss normal distribution, we mean symmetrically dispersed data. As all measures with a central tendency fall in the middle, the symmetrical distribution has zero skewness.

• Kurtosis:- The degree to which outliers are present in the distribution is referred to as kurtosis. Whether the data in a normal distribution have heavy or light tails, kurtosis is a statistical metric. Kurtosis is a metric for financial risk used in finance.

• A significant kurtosis suggests that there is a high likelihood of both extremely large and extremely tiny returns, which is correlated with a high level of risk for an investment. A small kurtosis, on the other hand, indicates a moderate amount of risk because the likelihood of extreme returns is relatively low.

Hence, Yes, this is true that skewness and kurtosis is independent of charge of origin and scale

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