Graphical Summary

Table of Statistics - Skewness and Kurtosis

  

Skewness

Skewness refers to a lack of symmetry. A distribution is skewed if one tail extends farther than the other. A skewness statistic is provided with the graphical summary:

·    A value close to 0 indicates symmetric data.

·    Negative values imply negative/left skew.

·    Positive values indicate positive/right skew.

Kurtosis

Kurtosis refers to how sharply peaked a distribution is. A kurtosis statistic is provided with the graphical summary:

Values close to 0 indicate normally peaked data.

·    Negative values indicate a distribution that is flatter than normal.

·    Positive values indicate a distribution with a sharper than normal peak.

Example Output

 

Anderson-Darling Normality Test

 A-Squared: 0.99

 P-Value: 0.008

 

 Mean 3.6364

 StDev 2.3779

 Variance 5.6545

 Skewness 2.11078

 Kurtosis 5.61936

 N 11

 

 Minimum 1.0000

 1st Quartile 2.0000

 Median 3.0000

 3rd Quartile 4.0000

 Maximum 10.0000

 

 

The skewness and kurtosis values are listed in the middle right of the Graphical Summary.

Interpretation

The skewness value for the precipitation data is 2.11078 indicating that the distribution is right-skewed. This is due to the outlier shown at the far right of the histogram.

The kurtosis value for the precipitation data is 5.61936 indicating that the distribution is more sharply peaked than normal. This is illustrated in the histogram that shows that the peak of the data rises well above the normal curve (blue).