Skewness:
Skewness measures the asymmetry of a distribution. It tells you whether the data is skewed to the left (negatively skewed), to the right (positively skewed), or is approximately symmetric. In a positively skewed distribution, the tail on the right side is longer or fatter than the left side, and the majority of the data points are concentrated on the left side. In a negatively skewed distribution, the tail on the left side is longer or fatter than the right side, and the majority of the data points are concentrated on the right side. A perfectly symmetric distribution has a skewness of zero.
Kurtosis:
Kurtosis measures the “tailedness” of a distribution, indicating whether the data has heavy tails (leptokurtic) or light tails (platykurtic) compared to a normal distribution. A positive kurtosis (leptokurtic) indicates that the distribution has heavier tails and a more peaked central region than a normal distribution. A negative kurtosis (platykurtic) indicates that the distribution has lighter tails and a flatter central region than a normal distribution. A normal distribution has a kurtosis of 3 (excess kurtosis), so any deviation from this value (greater or smaller) indicates the degree of departure from normality.
We calculated these before combining the data and after combining the data.