Kurtosis tells you the height and sharpness of the central peak, relative to that of a standard bell curve. Therefore, the excess kurtosis is found using the formula below: Excess Kurtosis = Kurtosis – 3 . When a set of approximately normal … The kurtosis of the normal distribution is 3, which is frequently used as a benchmark for peakedness comparison of a given unimodal probability density. Kurtosis is measured by moments and is given by the following formula − Formula If a curve is less outlier prone (or lighter-tailed) than a normal curve, it is called as a platykurtic curve. MATH200B Program — Extra Statistics Utilities for TI-83/84 has a program to download to your TI-83 or TI-84. Mesokurtic: This is the normal distribution; Leptokurtic: This distribution has fatter tails and a sharper peak.The kurtosis is “positive” with a value greater than 3; Platykurtic: The distribution has a lower and wider peak and thinner tails.The kurtosis is “negative” with a value greater than 3 The kurtosis of a normal distribution equals 3. This means the kurtosis is the same as the normal distribution, it is mesokurtic (medium peak).. Here, x̄ is the sample mean. Let’s see the main three types of kurtosis. If the curve of a distribution is more outlier prone (or heavier-tailed) than a normal or mesokurtic curve then it is referred to as a Leptokurtic curve. When kurtosis is equal to 0, the distribution is mesokurtic. With this definition a perfect normal distribution would have a kurtosis of zero. Scenario This property makes Kurtosis largely ignorant about the values lying toward the center of the distribution, and it makes Kurtosis sensitive toward values lying on the distribution’s tails. A negative value indicates a distribution which is more peaked than normal, and a positive kurtosis indicates a shape flatter than normal. A normal distribution has skewness and excess kurtosis of 0, so if your distribution is close to those values then it is probably close to normal. Types of Kurtosis. KURTOSIS. https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm The kurtosis of a mesokurtic distribution is neither high nor low, rather it is considered to be a baseline for the two other classifications. Here it doesn’t (12.778), so this distribution is also significantly non normal in terms of Kurtosis (leptokurtic). The normal distribution has a kurtosis value of 3. The orange curve is a normal distribution. Kurtosis is a measure of the combined weight of a distribution's tails relative to the center of the distribution. This definition of kurtosis can be found in Bock (1975). The only difference between formula 1 and formula 2 is the -3 in formula 1. The types of kurtosis are determined by the excess kurtosis of a particular distribution. The following diagram gives a general idea of how kurtosis greater than or less than 3 corresponds to non-normal distribution shapes. The normal PDF is also symmetric with a zero skewness such that its median and mode values are the same as the mean value. BREAKING DOWN Kurtosis . The entropy of a normal distribution is given by 1 2 log e 2 πe σ 2. The "minus 3" at the end of this formula is often explained as a correction to make the kurtosis of the normal distribution equal to zero, as the kurtosis is 3 for a normal distribution. Notice that kurtosis greater than or less than 3 corresponds to non-normal distribution shapes. 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