Summary Statistics

Description

Summary Statistics produces various statistics such as: Mean, Median, Standard Deviation, Min, Max, Range, Standard Error, Counts, Skew, kurtosis, etc to summarise a set of observations in a dataset.

Inputs

summarystatsinput

[Click to Enlarge]

Dataset Input: a dataset containing the variable(s) to be tested.

Variable Name: the variable(s) to be tested – more than one can be selected.

[Analyse your data > Tools > Statistical Analysis > Summary Statistics > enter parameters > Add Tools > Show/Hide > Execute]

Outputs

A summary of all of the variables:

Num rows: Number of rows (observations) in the data

Num cols: Number of columns (variables/attributes) in the data

Means: Mean value(s) of the variables/attributes

Medians: Median value(s) of the variable/attributes

Quantiles: 1st (“25%”) and 3rd (75%) quartile values for each of the columns (variables/attributes)

Storage mode: The type of value that each variable/attribute is recorded as: logical, i.e. “TRUE” or “FALSE”; integer, i.e. 1, 2, -1, 7; double, i.e. 1.03472, 2.49227; and character i.e. “green”, “red”.

A summary for each of the variables/attributes:

Sd: Standard deviation for a variable/attribute1

Trimmed: Trimmed mean value for a variable/attribute calculated by dropping a proportion of the observations from both ends of the sample, and can allow you to determine whether long tails/outliers have an impact on the mean value2

Mad: Median absolute deviation3, is the median value of the residuals of each observation from the sample median for each variable/attribute

Min: The minimum value for a variable/attribute

Max: The maximum value for a variable/attribute

Range: The range of values (Max – Min) for a variable/attribute

Skew: The measure of how much a range of values is skewed to the left or right of the mean (asymmetry) for a variable/attribute4

Kurtosis: The measure of how flat or “chopped off” a range of values is around the mean for a variable/attribute5

Se: The standard error of the mean for a variable/attribute6

Missing: number of missing values for a variable/attribute

Unique: number of distinct values for a variable/attribute

Breaks: Break classes defined by min and max of a variable/attribute with an interval of 5%

Counts: Number of observations/rows in each break class for a variable/attribute

Density: The percentage of observations/rows within each break class for a variable/attribute

Mids: The median value within each break class for a variable/attribute

Equidistant: Indicating if the distances between breaks are all the same for a variable/attribute (true/false)

References