Skewness is the measure of the asymmetry of the probability distribution of a random variable around its mean (average). The normal distribution almost does not have any skewness and is used as a reference for measurements. The value of skewness of normal distribution is nearly but not exactly zero, as the distribution curve is almost perfect but not absolutely symmetrical.

There are two types of skewness:

1. Positive Skewness: It can be identified by a tail on the right side. The value of the skewed distribution is greater than zero. For positive skewness, the mean value is greater than the median, which is greater than the mode.
2. Negative Skewness: It is the probability distribution with the tail on the left side. The value of the skewed distribution is less than zero. Moreover, the value of its mean is lesser than the median, and the median is lesser than the mode.

To have a precise and clear understanding of skewness, students need to learn a few things first.

• Probability theory
• Probability distribution
• Relationship between mean and median
• Standard deviation

The following is covered by the homework writing help to explain skewness.

• Skewness definition
• Types
• Normal distribution, positive distribution and negative distribution
• Pearson’s moment coefficient of skewness
• Pearson’s mode skewness and Pearson’s median skewness.
• Other measures of skewness
• Application and how to interpret skewness

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