dis-cuss the skewness of the distribution.

Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. It can take the following forms:

1. Positive Skewness: This occurs when the tail on the right side of the distribution is longer or fatter than the left side. It means that the data are heavy-tailed or profusion of larger values.

2. Negative Skewness: This occurs when the tail of the left side of the distribution is longer or fatter than the right side. It indicates a data are heavy-tailed or profusion of smaller values.

3. No Skewness: A distribution with no skewness or zero skewness is referred to as a symmetric distribution. That is, the distribution is perfectly symmetrical around its mean.

To determine the skewness of the distribution, you can use the formula:

Skewness = 3 * (Mean – Median) / Standard Deviation

If the skewness is less than -1 or greater than 1, the distribution is highly skewed. If the skewness is between -1 and -0.5 or between 0.5 and 1, the distribution is moderately skewed. If the skewness is between -0.5 and 0.5, the distribution is approximately symmetric.

In conclusion, skewness is a critical concept in understanding the distribution of your data. It helps you to identify the shape of the distribution of your data, whether the distribution is symmetrical or asymmetrical.