In probability theory and statistics, the Skewness Formula is a measure of the asymmetry of a real-valued random variable’s probability distribution with respect to its mean. A positive, zero, negative, or undefined value for the skewness can be used.
Positive skew typically indicates that the tail is on the right side of a unimodal distribution, while negative skew typically indicates that the tail is on the left. The Skewness Formula does not follow a straightforward rule when one tail is long and the other tail is fat. For instance, a value of zero indicates that the overall balance between the tails on either side of the mean; This is true for symmetric distributions, but it can also be the case for asymmetric distributions with one tail that is short and fat and the other that is long and thin.
What is Skewness Formula?
When the graph plotted is displayed in a skewed form, the Skewness Formula is used to measure the asymmetry of a distribution. Skewness reveals the asymmetry of a probability distribution. Skewness Formula is a statistical measure to help reveal the asymmetry of a probability distribution. Asymmetry or distortion of a symmetric distribution is measured by skewness. It calculates how far a given random variable’s distribution deviates from a symmetric distribution like the normal distribution. Since it is symmetrical on both sides, a normal distribution has no skewness. Therefore, a curve is considered to be skewed if it is tilted to the right or left. Positive skewness refers to a distribution that is shifted to the left and has its tail on the right. The distribution is also referred to as being right-skewed. The tapering of the curve that differs from the data points on the other side is referred to as a tail.
A positively skewed distribution assumes a skewness value greater than zero, as the name suggests. The given distribution has a right-to-left skewness, which causes the mean value to be greater than the median and move to the right. The mode also occurs with the highest frequency in the distribution.
It is a negatively skewed distribution if the given distribution is shifted to the right and has its tail on the left. Another name for it is a left-skewed distribution. Any distribution with a negative skew has a skewness value that is less than zero. The given distribution has a left-handed skewness, which causes the mean value to be less than the median and move to the left. The mode also occurs most frequently in the distribution.
Solved Examples Using Skewness Formula
To answer the questions in the exercises, students must understand the Skewness Formula. The topics given in the chapters need to be thoroughly covered. Students would find it simpler to practice questions involving the Skewness Formula after learning theories. They are also advised to read the examples with solutions. Solved examples provide insight into various approaches to problem-solving. The Skewness Formula derivation must be understood by the students. It is important to pay attention to every subject on the syllabus. The study materials on Extramarks are useful for getting ready for the examinations. Skewness Formula is helpful in finding skewness of a given data. Students are supposed to keep practising questions in order to score well in the examination. Practising questions is crucial for retaining the Skewness Formula for a long time.