The mean of N measurements over the stated period.

The maximum value of N measurements over the stated period.

The minimum value of N measurements over the stated period.

The [number] of valid measurements over the stated period that contributed to the other statistical outputs.

The total [number] of valid measurements over the stated period, including observations that were ignored because they did not contain valid result values.

The median of N measurements over the stated period.

The standard deviation of N measurements over the stated period.

The sum of N measurements over the stated period.

The variance of N measurements over the stated period.

The 20th Percentile of N measurements over the stated period.

The 80th Percentile of N measurements over the stated period.

The lower Quartile Boundary of N measurements over the stated period.

The upper Quartile Boundary of N measurements over the stated period.

The difference between the upper and lower Quartiles is called the Interquartile range. (IQR = Q3-Q1) Quartile deviation or Semi-interquartile range is one-half the difference between the first and the third quartiles.

The lowest of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population.

The second of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population.

The third of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population.

The fourth of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population.

Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or even undefined. Source: Wikipedia.

Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. Source: Wikipedia.

Linear regression is an approach for modeling two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variables. Source: Wikipedia This Statistic code will return both a gradient and an intercept value.