Statistical Formula Support

Analysis and manipulation can be done on chart input data using Statistical formulas. This statistical information can be divided into Statistical Tests, Basic Statistical Formulas and Utility functions.

Anova

An ANOVA test is done to determine the existence of a statistically significant difference, between the means of two or more groups of data.

F Test Formula

The Ftest formula performs an F-test, using F distribution, and is used to determine whether two samples have same standard deviation with the specified confidence level.

Z Test Formula

The Z test is a statistical formula that is used to determine if the difference between a sample mean and the population mean is large enough to be statistically significant.

T Test Formula

T - Test Formula is a statistical significance test used to measure the equality between two sample means.

T-Test with Equal Variances Formula

Here, a T Test using Student's distribution (T distribution) with equal variances, is performed. T distribution formula returns the probability for the T distribution (student's distribution).

T-Test Paired Formula

Here, a T Test using Student's distribution (T distribution) with paired samples, is performed. This test comes in handy when a sample group is tested twice.

T-Test with UnEqual Variances Formula

Here, a T Test using Student's distribution (T distribution) with unequal variances is performed.

Basic Statistical Formula

The basic statistical functions always return a double value, and use one or two series for input. The basic statistical functions are:

Mean and Median

Mean returns the average or mean and median returns the mid - value of the data points in a series.

Standard deviation and Variance

Standard deviation is a statistical measure of variability. The square root of the average of the squares of deviations, about the mean of a set of data, is returned on using Standard Deviation.

Variance of a random variable is a measure of its statistical dispersion, indicating to what extent the values differ from the expected values. The variance of a real-valued random variable is its second central moment, and it also happens to be its second cumulant. The variance of a random variable is the square of its standard deviation.

Correlation and Covariance

Correlation is a statistical measure of the extent to which the movement of two securities or asset classes are related. The range of possible correlations is between -1 and +1. A result of -1 refers to a perfect negative correlation, +1 refers to a perfect positive correlation, and 0 means no correlation at all.

Covariance is a statistical measure used in computing the correlation coefficient between two variables; the covariance is the mean of X, X(bar) and Y, Y(bar) over all pairs of values for the variables x and y, where X(bar) and Y(bar) are the mean of X and Y values respectively.Summing up, Covariance is a statistical value measuring the simultaneous deviations of X and Y variables from their means.

Utility Functions

There are two utility functions to calculate distribution values: the Gamma and Beta function. These functions always return a double value and uses one or two double values for input.

BetaFunction and GammaFunction

The BetaFunction and Gamma Function methods return the respective Beta Function and Gamma function for a given value.

BetaCumulative Function and GammaCumulative Function

The BetaCumulative and GammaCumulative function methods return the BetaCumulative and the GammaCumulative functions, respectively, for a given value.