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  2. Autocorrelation - Wikipedia

    en.wikipedia.org/wiki/Autocorrelation

    For example, to calculate the autocorrelation of the real signal sequence = (,,) (i.e. =, =, =, and = for all other values of i) by hand, we first recognize that the definition just given is the same as the "usual" multiplication, but with right shifts, where each vertical addition gives the autocorrelation for particular lag values: +

  3. Breusch–Godfrey test - Wikipedia

    en.wikipedia.org/wiki/Breusch–Godfrey_test

    The Breusch–Godfrey test is a test for autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic is derived from these. The null hypothesis is that there is no serial correlation of any order up to p. [3]

  4. Wald–Wolfowitz runs test - Wikipedia

    en.wikipedia.org/wiki/Wald–Wolfowitz_runs_test

    The run test is based on the null hypothesis that each element in the sequence is independently drawn from the same distribution. Under the null hypothesis, the number of runs in a sequence of N elements [ note 1 ] is a random variable whose conditional distribution given the observation of N + positive values [ note 2 ] and N − negative ...

  5. Correlogram - Wikipedia

    en.wikipedia.org/wiki/Correlogram

    A plot showing 100 random numbers with a "hidden" sine function, and an autocorrelation (correlogram) of the series on the bottom. In the analysis of data, a correlogram is a chart of correlation statistics.

  6. Correlation function - Wikipedia

    en.wikipedia.org/wiki/Correlation_function

    In this definition, it has been assumed that the stochastic variables are scalar-valued. If they are not, then more complicated correlation functions can be defined. For example, if X(s) is a random vector with n elements and Y(t) is a vector with q elements, then an n×q matrix of correlation functions is defined with , element

  7. Geary's C - Wikipedia

    en.wikipedia.org/wiki/Geary's_C

    Geary's C is a measure of spatial autocorrelation that attempts to determine if observations of the same variable are spatially autocorrelated globally (rather than at the neighborhood level). Spatial autocorrelation is more complex than autocorrelation because the correlation is multi-dimensional and bi-directional.

  8. Durbin–Watson statistic - Wikipedia

    en.wikipedia.org/wiki/Durbin–Watson_statistic

    In statistics, the Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. It is named after James Durbin and Geoffrey Watson .

  9. Bartlett's test - Wikipedia

    en.wikipedia.org/wiki/Bartlett's_test

    The test procedure due to M.S.E (Mean Square Error/Estimator) Bartlett test is represented here. This test procedure is based on the statistic whose sampling distribution is approximately a Chi-Square distribution with ( k − 1) degrees of freedom, where k is the number of random samples, which may vary in size and are each drawn from ...