Explaining skewness and kurtosis

In statistical analysis, a moment is a quantitative measure that describes the expected distance from a reference point. If the reference point is expected, then it's called a central moment. In statistics, the central moments are the moments that are related with the mean. The first and second moments are the mean and the variance, respectively. The mean is the average of your data points. The variance is the total deviation of each data point from the mean. In other words, the variance shows how your data is dispersed from the mean. The third central moment is skewness, which measures the asymmetry of the distribution of the mean. In standard normal distribution, skewness equals zero as it's symmetrical. On the other hand, if mean < median < mode, then there is negative skew, or left skew; likewise, if mode < median < mean...