Statistics Calculator

This calculator can statistically evaluate data sets of numbers. The data must be available as text, and the individual values must be separated by a separator. The default is the semicolon; separators that cannot be used are periods and commas, as these are used as decimal separators. Special characters can cause problems, so ; is recommended.
Different scale levels can be set, which then allow different tests. For example, the mean values can only be calculated sensibly for interval-scaled or ratio-scaled data. The median, i.e. the middle value, and the quantiles require at least an ordinal scale, whereas the modal value, which is the most common value, can be determined at any scale level. This also applies to the number of values, of course. If the scale level is ignored, all available tests are carried out, and you then have to take care which of them are useful.
The chi-square test is used to determine how well the data corresponds to a uniform distribution, i.e. to what extent each value has the same chance of appearing. The Kolmogorov-Smirnov test, on the other hand, tests for a normal distribution in which the middle values are more frequent and those at the edges are less frequent. A simple bar chart is displayed for the frequency distribution.

Data set for the statistics (only numbers and separators):

Separator: Positions after decimal point: Level of measurement:
|

Arithmetic mean (average): | Geometric mean: | Harmonic mean:
Median: | Mode: | Minimum: | Maximum: | Max./Min.: -
Range: - | Quantil Q₂₅: | Quantil Q₇₅: | Interquartile range: -
Number of values: | Different values:
Mean deviation from the median: | Median absolute deviation:
Variance: | Standard deviation:

Chi-square-test (Χ²) for uniform distribution: | Degrees of freedom:
No significant difference from the uniform distribution.
For discret values, only the frequency must be entered. If a value doesn't exist enter 0.
Example for 5 values: 25;32;29;27;35 | Maximal degrees of freedom: 100

Kolmogorow-Smirnow test for normal distribution: -

Frequency distribution:

Value
Quantity