I am using SPSS as statistical analysis tool for my data set. I have few queries on kurtosis concept and the one generated by SPSS and excel. Please correct the understandings below and follow up questions:
Kurtosis as a measure of flatness or peakness (hump) around the mean in the distribution. In terms of distribution tails, it tells whether the dataset is heavy-tailed or light-tailed relative to a normal distribution.
A normal distribution has kurtosis exactly 3 (excess kurtosis exactly 0 which is kurt-3) and also called as mesokurtic distribution. A distribution with high kurtosis will have its peak bigger than mesokurtic peak and is called as leptokurtic A distribution with low kurtosis will have its peak smaller than mesokurtic peak and is called as platykurtic.
Questions:
What does it mean by excess kurtosis and what is the significance of using it? I am not getting clear picture between kurtosis vs excess kurtosis except that excess kurtosis is kurtosis-3 so that we take 0 as baseline.
SPSS tool generates "excess kurtosis" values or simple "kurtosis" values? In other words what baseline we generally consider in SPSS for kurtosis measurement and inference? Is it 0 or 3? In SPSS I am getting kurtosis of 1.16. So if I consider 3 as baseline then 1.16 is less than 3 and so my distribution could be platykurtic. But if I consider baseline as 0 (excess kurtosis), then 1.16 is clearly greater than 0 and so my distribution could be leptokurtic.
How it works out in excel again? Does the excel formula internally compute kurtosis as (kurt - 3) or simple kurt? I mean how to infer the result in MS excel too (baseline 3 or 0)?
Kurtosis does not measure "peakedness" or "height" of a distribution. It measures (potential) outliers (rare, extreme observations) only. For a clear explanation, please see here: https://en.wikipedia.org/wiki/Talk:Kurtosis#Why_kurtosis_should_not_be_interpreted_as_.22peakedness.22