Note: This is documentation for version 4.11 of Source. For a different version of Source go to the Space Directory.
##### Page tree
Go to start of banner

# Univariate Statistics SRG

Univariate statistics provide information on a single variable. They summarise and reveal patterns in that variable. In Source, the variable used to calculate statistics is a time series. Statistics are calculated in Results Manger on the Statistics tab, see Chart Statistics.
The types of univariate statistics available in Source are described in Table 1.

###### Table 1. Univariate Statistics

Statistic

Definition

Example for
[-9999, 0, 1, 3, 5, 9, 9]

Minimum

Minimum value in the time series.

0

Maximum

Maximum value in the time series.

9

Number of Values

The number of values in the time series, not including nulls.

6

Number of Nulls

The number of nulls, either missing values or values entered as -9999. These values are ignored in all other univariate statistics.

1

Total

The sum of all values in the time series.

27

Mean

The sum of all values in the time series divided by the number of values,

5

Median

The middle value in the sorted list of all values in a time series. For n values, the middle value is . When n is even, the median is the mean of the two middle values.

4

Standard Deviation

How widely values in the time series vary from the mean. See Standard Deviation.

3.89 (to 2 decimal places)

Skew

The skewness of the distribution of values in the time series. See Skew.

0.23 (to 2 decimal places).

## Standard Deviation

### Definition

The standard deviation (s) measures the amount by which values in the time series vary from the mean. It is defined as:

 Equation 1 Where:

x is the value of time series x at time step i is the mean of time series x
n  is the number of values in time series x.

### Interpretation

A low (smaller) standard deviation, indicates the values are close to the mean, with a narrow range; a high standard deviation indicates the values are spread out over a wider range.

## Skew

### Definition

The skew measures the degree of asymmetry of a distribution around its mean. It is defined as:

 Equation 2 Where all terms are defined in Equation 1.

### Interpretation

A symmetrical dataset will have a skew of 0. A positive skew indicates a distribution with an asymmetric tail extending toward values greater than the mean. Negative skew indicates a distribution with an asymmetric tail extending toward values less than the mean.