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  2. Variance decomposition of forecast errors - Wikipedia

    en.wikipedia.org/wiki/Variance_decomposition_of...

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  3. White test - Wikipedia

    en.wikipedia.org/wiki/White_test

    In R, White's Test can be implemented using the white function of the skedastic package. [5]In Python, White's Test can be implemented using the het_white function of the statsmodels.stats.diagnostic.het_white [6]

  4. Seemingly unrelated regressions - Wikipedia

    en.wikipedia.org/wiki/Seemingly_unrelated...

    Suppose there are m regression equations = +, =, …,. Here i represents the equation number, r = 1, …, R is the individual observation, and we are taking the transpose of the column vector.

  5. 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.

  6. Factor analysis - Wikipedia

    en.wikipedia.org/wiki/Factor_analysis

    To interpret the results, ... The data for multiple products is coded and input into a statistical program such as R, SPSS, SAS, Stata, STATISTICA, JMP, and SYSTAT.

  7. Hosmer–Lemeshow test - Wikipedia

    en.wikipedia.org/wiki/Hosmer–Lemeshow_test

    The Hosmer–Lemeshow test is a statistical test for goodness of fit and calibration for logistic regression models. It is used frequently in risk prediction models. The test assesses whether or not the observed event rates match expected event rates in subgroups of the model population.

  8. Box's M test - Wikipedia

    en.wikipedia.org/wiki/Box's_M_test

    This statistics -related article is a stub. You can help Wikipedia by expanding it.

  9. Wald test - Wikipedia

    en.wikipedia.org/wiki/Wald_test

    In statistics, the Wald test (named after Abraham Wald) assesses constraints on statistical parameters based on the weighted distance between the unrestricted estimate and its hypothesized value under the null hypothesis, where the weight is the precision of the estimate.