Calculate skew and kurtosis, standard errors for both, and the estimates divided by two times the standard error. If this latter quantity exceeds an absolute value of 1, the skew/kurtosis is significant. With very large sample sizes, significant skew/kurtosis is common.
skew_kurtosis(x, verbose = FALSE, se = FALSE, ...)An object for which a method exists.
Logical. Whether or not to print messages to the console, Default: FALSE
Whether or not to return the standard errors, Default: FALSE
Additional arguments to pass to and from functions.
A matrix of skew and kurtosis statistics for x.
skew_kurtosis(datasets::anscombe)
#> skew skew_2se kurt kurt_2se
#> x1 0.00000000 0.00000000 1.780000 0.6956300
#> x2 0.00000000 0.00000000 1.780000 0.6956300
#> x3 0.00000000 0.00000000 1.780000 0.6956300
#> x4 2.84604989 2.15385490 9.100000 3.5563107
#> y1 -0.05580807 -0.04223484 2.179061 0.8515845
#> y2 -1.12910800 -0.85449479 3.007674 1.1754091
#> y3 1.59223074 1.20498027 5.130453 2.0049984
#> y4 1.29302529 0.97854534 4.390789 1.7159351