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Kurtosis

As explained in Statistics Slam Dunk

Kurtosis is a statistic describing the tail heaviness or peakedness of a probability distribution relative to a reference distribution.

Kurtosis measures how long the tails of a distribution are. Values near 0 indicate a normal distribution; negative values indicate thin tails, while positive values indicate fat tails. section 17.4.1

Use kurtosis during exploratory data analysis to judge distribution shape:

  • Read kurtosis beside skewness in continuous-variable summaries; together, they indicate whether a distribution resembles a normal distribution. section 17.4.1
  • Treat values close to zero as consistent with normality, and values farther from zero as evidence of thinner or fatter tails. section 17.4.3
  • Compare the statistic with QQ plots and density plots rather than interpreting it in isolation. section 17.4.3

NBA odds data

A tableone summary reports kurtosis for variables such as ptsR, openspreadR, and closespreadH. SmartEDA extends the check with density plots and QQ plots, showing values near zero for normally distributed openspreadH and more distant values for non-normal diff_margin_closespreadH. section 17.4.1 section 17.4.3

options(scipen = 999)
summary(tableOne)
## 
##      ### Summary of continuous variables ###
## 
## strata: Overall
##                 n miss p.miss mean sd median p25 p75 min max skew kurt
## ptsR         1230    0      0  110 12    110 101 118  68 168  0.1  0.2
## openspreadR  1230    0      0    2  6      3  -2   8 -16  18 -0.2 -0.6
## opentotal    1230    0      0  222  9    222 216 228 196 243 -0.2 -0.4
## closespreadR 1230    0      0    3  7      4  -2   8 -17  18 -0.2 -0.7
## closetotal   1230    0      0  222  9    222 216 228 194 244 -0.1 -0.3
## ptsH         1230    0      0  113 13    112 104 121  77 161  0.2  0.1
## openspreadH  1230    0      0   -2  6     -3  -8   2 -18  16  0.2 -0.6
## closespreadH 1230    0      0   -3  7     -4  -8   2 -18  17  0.2 -0.7
## 
## ======================================================================
## 
##      ### Summary of categorical variables ### 
## 
## strata: Overall
##     var    n miss p.miss         level freq percent cum.percent
##   teamR 1230    0    0.0       Atlanta   41     3.3         3.3
##                                 Boston   41     3.3         6.7
##                               Brooklyn   41     3.3        10.0

##                                Toronto   41     3.3        93.3
##                                   Utah   41     3.3        96.7
##                             Washington   41     3.3       100.0
##                                                                
##  venueR 1230    0    0.0             R 1230   100.0       100.0
##                                                                
##   teamH 1230    0    0.0       Atlanta   41     3.3         3.3
##                                 Boston   41     3.3         6.7
##                               Brooklyn   41     3.3        10.0

##                                Toronto   41     3.3        93.3
##                                   Utah   41     3.3        96.7
##                             Washington   41     3.3       100.0
##                                                                
##  venueH 1230    0    0.0             H 1230   100.0       100.0
##                                                                
##  month2 1230    0    0.0             1  110     8.9         8.9
##                                      2  219    17.8        26.7
##                                      3  219    17.8        44.6
##                                      4  221    18.0        62.5
##                                      5  158    12.8        75.4
##                                      6  224    18.2        93.6
##                                      7   79     6.4       100.0
##

Inspect the continuous-variable summary, including sample size, mean, spread, skewness, and kurtosis for NBA odds variables. section 17.4.1

How Statistics Slam Dunk covers it

How deep it goes
Defines values near zero, negative values, and positive values, but stops short of deriving the statistic.
Examples
Uses NBA odds variables such as ptsR, openspreadR, and closespreadH, summarized with tableone and visualized by SmartEDA.
What you can do after
Evaluate/understand distribution shape by interpreting kurtosis alongside skewness, QQ plots, and density plots.
How it connects
Connects kurtosis with skewness, normal distributions, summary statistics, density plots, and QQ plots.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.