Examples for

Descriptive Statistics

Descriptive statistics are statistical measures of a dataset that describe, characterize and summarize its properties, such as shape, variability, size and central location. Wolfram|Alpha's rigorous statistical algorithms enable you to compute and characterize the properties of your data with lightning-fast speed.

Summary Statistics

Compute elementary descriptive statistics summarizing the properties of a dataset, such as maximum and minimum values or number of entries.

Calculate basic descriptive statistics for a dataset:

{25, 35, 10, 17, 29, 14, 21, 31}
five number summary {31, 37, 55, 61, 73, 84}

Measures of Dispersion

Compute the measures of dispersion, such as variance or standard deviation, for a dataset.

Compute the variance:

variance {21.3, 38.4, 12.7, 41.6}

Compute the standard deviation:

standard deviation 98.17, 112.3, 102.6, 94.3, 108.1

Measures of Central Tendency

Compute common measures of central tendency, such as mean, median and mode, for a dataset.

Compute the mean of a dataset:

mean {21.3, 38.4, 12.7, 41.6}

Compute the median:

median 98.17, 112.3, 102.6, 94.3, 108.1

Compute the geometric mean:

geometric mean 12, 17

Other Descriptive Statistics

Compute other common descriptive statistics, such as skewness, kurtosis and outliers, for a dataset.

Compute the skewness:

skewness 98.17, 112.3, 102.6, 94.3, 108.1

Compute the kurtosis:

kurtosis {21.3, 38.4, 12.7, 41.6}

Determine the outliers for a dataset:

what is the outlier for the data set 21.3, 22, 12.7, 11, 100

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