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  2. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

    Help; Learn to edit; Community portal; Recent changes; Upload file; Special pages

  3. Error bar - Wikipedia

    en.wikipedia.org/wiki/Error_bar

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

  4. Galbraith plot - Wikipedia

    en.wikipedia.org/wiki/Galbraith_plot

    Galbraith plots are available within the metafor package in R, along with various other diagnostic and summary plots. MIX 2.0 Software to perform meta-analysis and create Galbraith plots in Excel. RadialPlotter Java application for fission track, luminescence and other radial plots from P. Vermeesch.

  5. Heteroskedasticity-consistent standard errors - Wikipedia

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

    Heteroskedasticity-consistent standard errors that differ from classical standard errors may indicate model misspecification. Substituting heteroskedasticity-consistent standard errors does not resolve this misspecification, which may lead to bias in the coefficients. In most situations, the problem should be found and fixed. [5]

  6. Reduced chi-squared statistic - Wikipedia

    en.wikipedia.org/wiki/Reduced_chi-squared_statistic

    The standard deviation is the square root of the variance. When individual determinations of an age are not of equal significance, it is better to use a weighted mean to obtain an "average" age, as follows: x ¯ ∗ = ∑ i = 1 N w i x i ∑ i = 1 N w i . {\displaystyle {\overline {x}}^{*}={\frac {\sum _{i=1}^{N}w_{i}x_{i}}{\sum _{i=1}^{N}w_{i}}}.}

  7. Residual sum of squares - Wikipedia

    en.wikipedia.org/wiki/Residual_sum_of_squares

    The general regression model with n observations and k explanators, the first of which is a constant unit vector whose coefficient is the regression intercept, is = + where y is an n × 1 vector of dependent variable observations, each column of the n × k matrix X is a vector of observations on one of the k explanators, is a k × 1 vector of true coefficients, and e is an n× 1 vector of the ...

  8. Proportionate reduction of error - Wikipedia

    en.wikipedia.org/wiki/Proportionate_reduction_of...

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  9. Tanner graph - Wikipedia

    en.wikipedia.org/wiki/Tanner_graph

    Tanner proved the following bounds Let be the rate of the resulting linear code, let the degree of the digit nodes be and the degree of the subcode nodes be .If each subcode node is associated with a linear code (n,k) with rate r = k/n, then the rate of the code is bounded by

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