For kurtosis, the general guideline is that if the number is greater than +1, the distribution is too peaked. Likewise, a kurtosis of less than –1 indicates a distribution that is too flat. Distributions exhibiting skewness and/or kurtosis that exceed these guidelines are considered nonnormal.” (Hair et al., 2017, p.
Contents
How do you interpret kurtosis results?
If the kurtosis is greater than 3, then the dataset has heavier tails than a normal distribution (more in the tails). If the kurtosis is less than 3, then the dataset has lighter tails than a normal distribution (less in the tails).
What does the kurtosis value tell us?
Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with low kurtosis tend to have light tails, or lack of outliers.
How do you interpret kurtosis and skewness?
For skewness, if the value is greater than + 1.0, the distribution is right skewed. If the value is less than -1.0, the distribution is left skewed. For kurtosis, if the value is greater than + 1.0, the distribution is leptokurtik. If the value is less than -1.0, the distribution is platykurtik.
What is a good kurtosis?
A standard normal distribution has kurtosis of 3 and is recognized as mesokurtic. An increased kurtosis (>3) can be visualized as a thin “bell” with a high peak whereas a decreased kurtosis corresponds to a broadening of the peak and “thickening” of the tails. Kurtosis >3 is recognized as leptokurtic and <3.
What if kurtosis is negative?
A distribution with a negative kurtosis value indicates that the distribution has lighter tails than the normal distribution. For example, data that follow a beta distribution with first and second shape parameters equal to 2 have a negative kurtosis value.
How do you read kurtosis in SPSS?
How to Calculate Skewness and Kurtosis in SPSS
- Click on Analyze -> Descriptive Statistics -> Descriptives.
- Drag and drop the variable for which you wish to calculate skewness and kurtosis into the box on the right.
- Click on Options, and select Skewness and Kurtosis.
- Click on Continue, and then OK.
What is good skewness and kurtosis?
The values for asymmetry and kurtosis between -2 and +2 are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). Hair et al. (2010) and Bryne (2010) argued that data is considered to be normal if skewness is between ‐2 to +2 and kurtosis is between ‐7 to +7.
Is kurtosis a measure of dispersion?
The kurtosis can now be seen as a measure of the dispersion of Z2 around its expectation. Alternatively it can be seen to be a measure of the dispersion of Z around +1 and −1. κ attains its minimal value in a symmetric two-point distribution.
What does positive kurtosis look like?
What does it mean when kurtosis is positive? Positive excess values of kurtosis (>3) indicate that a distribution is peaked and possess thick tails.A leptokurtic distribution has a higher peak (thin bell) and taller (i.e. fatter and heavy) tails than a normal distribution.
Is low kurtosis good?
Kurtosis is only useful when used in conjunction with standard deviation. It is possible that an investment might have a high kurtosis (bad), but the overall standard deviation is low (good). Conversely, one might see an investment with a low kurtosis (good), but the overall standard deviation is high (bad).
What is a high kurtosis score?
High kurtosis in a data set is an indicator that data has heavy tails or outliers. If there is a high kurtosis, then, we need to investigate why do we have so many outliers. It indicates a lot of things, maybe wrong data entry or other things.
How do you interpret kurtosis in Excel?
When interpreting kurtosis, the normal distribution is used a reference. A positive kurtosis implies a distribution with more extreme possible data values (outliers) than a normal distribution thus fatter tails (Leptokurtic distributions).
What is a large kurtosis?
Kurtosis is a statistical measure that defines how heavily the tails of a distribution differ from the tails of a normal distribution.A large kurtosis is associated with a high risk for an investment because it indicates high probabilities of extremely large and extremely small returns.
What does a kurtosis of 5 mean?
Distributions with large kurtosis exhibit tail data exceeding the tails of the normal distribution (e.g., five or more standard deviations from the mean). Distributions with low kurtosis exhibit tail data that are generally less extreme than the tails of the normal distribution.
How do you find kurtosis in statistics?
Kurtosis = Fourth Moment / Second Moment2
- Kurtosis = 313209 / (365)2
- Kurtosis = 2.35.