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  2. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

    For correlated random variables the sample variance needs to be computed according to the Markov chain central limit theorem.. Independent and identically distributed random variables with random sample size

  3. Welch's t-test - Wikipedia

    en.wikipedia.org/wiki/Welch's_t-test

    t. -test. In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the (null) hypothesis that two populations have equal means. It is named for its creator, Bernard Lewis Welch, and is an adaptation of Student's t -test, [1] and is more reliable when the two samples have unequal variances ...

  4. Student's t-test - Wikipedia

    en.wikipedia.org/wiki/Student's_t-test

    The difference between the two sample means, each denoted by X i, which appears in the numerator for all the two-sample testing approaches discussed above, is ¯ ¯ = The sample standard deviations for the two samples are approximately 0.05 and 0.11, respectively. For such small samples, a test of equality between the two population variances ...

  5. Jackknife resampling - Wikipedia

    en.wikipedia.org/wiki/Jackknife_resampling

    In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling . It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap. Given a sample of size , a jackknife estimator can be built by aggregating the ...

  6. Clustered standard errors - Wikipedia

    en.wikipedia.org/wiki/Clustered_standard_errors

    Clustered standard errors assume that is block-diagonal according to the clusters in the sample, with unrestricted values in each block but zeros elsewhere. In this case, one can define X c {\displaystyle X_{c}} and Ω c {\displaystyle \Omega _{c}} as the within-block analogues of X {\displaystyle X} and Ω {\displaystyle \Omega } and derive ...

  7. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    This t-statistic can be interpreted as "the number of standard errors away from the regression line." Regressions. In regression analysis, the distinction between errors and residuals is subtle and important, and leads to the concept of studentized residuals. Given an unobservable function that relates the independent variable to the dependent ...

  8. Tukey's range test - Wikipedia

    en.wikipedia.org/wiki/Tukey's_range_test

    Tukey's range test. Tukey's range test, also known as Tukey's test, Tukey method, Tukey's honest significance test, or Tukey's HSD ( honestly significant difference) test, [1] is a single-step multiple comparison procedure and statistical test. It can be used to correctly interpret the statistical significance of the difference between means ...

  9. Heteroskedasticity-consistent standard errors - Wikipedia

    en.wikipedia.org/wiki/Heteroskedasticity...

    The topic of heteroskedasticity-consistent ( HC) standard errors arises in statistics and econometrics in the context of linear regression and time series analysis. These are also known as heteroskedasticity-robust standard errors (or simply robust standard errors ), Eicker–Huber–White standard errors (also Huber–White standard errors or ...