Search results
Results from the WOW.Com Content Network
The Shapiro–Wilk test tests the null hypothesis that a sample x 1, ..., x n came from a normally distributed population. The test statistic is = (= ()) = (¯), where with parentheses enclosing the subscript index i is the ith order statistic, i.e., the ith-smallest number in the sample (not to be confused with ).
Simple back-of-the-envelope test takes the sample maximum and minimum and computes their z-score, or more properly t-statistic (number of sample standard deviations that a sample is above or below the sample mean), and compares it to the 68–95–99.7 rule: if one has a 3σ event (properly, a 3s event) and substantially fewer than 300 samples, or a 4s event and substantially fewer than 15,000 ...
Lilliefors test is a normality test based on the Kolmogorov–Smirnov test.It is used to test the null hypothesis that data come from a normally distributed population, when the null hypothesis does not specify which normal distribution; i.e., it does not specify the expected value and variance of the distribution. [1]
Let () be the -th ordered value from our size-sample. For example, if the sample consists of the values {,,,}, () =, because that is the second-lowest value.Let : be the mean of the th order statistic when making independent draws from a normal distribution.
The Anderson–Darling test is a statistical test of whether a given sample of data is drawn from a given probability distribution.In its basic form, the test assumes that there are no parameters to be estimated in the distribution being tested, in which case the test and its set of critical values is distribution-free.
ALGLIB includes an implementation of the Jarque–Bera test in C++, C#, Delphi, Visual Basic, etc.; gretl includes an implementation of the Jarque–Bera test; Julia includes an implementation of the Jarque-Bera test JarqueBeraTest in the HypothesisTests package.
In statistics, D'Agostino's K 2 test, named for Ralph D'Agostino, is a goodness-of-fit measure of departure from normality, that is the test aims to gauge the compatibility of given data with the null hypothesis that the data is a realization of independent, identically distributed Gaussian random variables.
The Brown–Forsythe test uses the median instead of the mean in computing the spread within each group (¯ vs. ~, above).Although the optimal choice depends on the underlying distribution, the definition based on the median is recommended as the choice that provides good robustness against many types of non-normal data while retaining good statistical power. [3]