Skewness measures the degree to which a distribution is skewed. It is calculated by finding the mean minus the median, divided by the standard deviation. A posi...
Skewness measures the degree to which a distribution is skewed. It is calculated by finding the mean minus the median, divided by the standard deviation. A positive skewness indicates that the distribution is skewed left, while a negative skewness indicates that the distribution is skewed right.
Kurtosis measures the degree to which a distribution is peaked or flattened. It is calculated by finding the third moment divided by the square of the standard deviation. A positive kurtosis indicates that the distribution is peaked, while a negative kurtosis indicates that the distribution is flatter.
In general, a skewness coefficient of 0 indicates a normal distribution, while a skewness coefficient greater than 0 indicates a skewed distribution to the right, and a skewness coefficient less than 0 indicates a skewed distribution to the left.
Kurtosis is similar to skewness, but it is calculated using the third central moment instead of the mean. A kurtosis coefficient of 0 indicates a symmetrical distribution, while a kurtosis coefficient greater than 0 indicates a peaked distribution, and a kurtosis coefficient less than 0 indicates a flattened distribution.
Skewness and kurtosis are important measures of central tendency and dispersion in statistical analysis. They can be used to identify outliers in a dataset and to assess the shape of a distribution