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Outlier detection

Command:Statistics
Next selectOutlier detection

Description

Outlier detection is used to detect anomalous observations in sample data.

Required input

Dialog box for outlier detection

Variable: the name of the variable containing the data to be analyzed.

Filter: (optionally) a filter in order to include only a selected subgroup of cases in the statistical analysis.

Methods of outlier detection:

Options

Results

Outlier detection - results
Outlier detection

Variable

Vit_E_Intake
Vit E Intake

Back-transformed after logarithmic transformation.

Sample size

54

Lowest value

0.7800

Highest value

407.4800

Geometric mean

10.1834

Median

8.1249

Coefficient of Skewness

1.1817 (P=0.0011)

Coefficient of Kurtosis

1.9972 (P=0.0248)

Shapiro-Francia test
for Normal distribution

W'=0.9000
reject Normality (P=0.0006)

Suspected outliers

Grubbs - double-sided (alpha-level 0.05)

None

Tukey, 1977

Outside values

208.51 225.88 407.48

Far-out values

None

Generalized ESD test (alpha-level 0.05)

208.51 225.88 407.48

Summary statistics

Suspected outliers

The program lists the outliers identified by the different procedures.

Grubbs' test can only be used to detect one single outlier; if you suspect there is more than one outlier you should not repeat the procedure but use the Generalized ESD test.

What to do when you have identified an outlier

Do not remove outliers automatically.

In all cases, report the outliers and how you have dealt with them.

Literature

See also