Question

What is skewness in data mining?

Answer 1

Answer:

Skewness is a numerical indicator of how far a data sample deviates from the normal distribution. If the data distribution's mean is smaller than the mode, there will be more graphed points to the left of the mode than to the right, resulting in a "negative skew."

Step-by-step explanation:

• In statistics, skewness refers to the degree of asymmetry in a probability distribution.
• To variable degrees, distributions can have right (positive) or left (negative) skewness. The skewness of a normal distribution (bell curve) is zero.
• When evaluating a return distribution, investors look for right-skewness, which, like excess kurtosis, better portrays the data set's extremes rather than relying just on the average.
• Skewness is a distortion or asymmetry in a collection of data that deviates from the symmetrical bell curve, or normal distribution.
• The curve is considered to be skewed if it is displaced to the left or right. Skewness may be expressed as a measure of how much a particular distribution deviates from a normal distribution.
• The skew of a normal distribution is zero, but a lognormal distribution, for example, has some right-skew.