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  2. Multicollinearity - Wikipedia

    en.wikipedia.org/wiki/Multicollinearity

    Including collinear variables does not reduce the predictive power or reliability of the model as a whole, [6] and does not reduce the accuracy of coefficient estimates. [ 1 ] High collinearity indicates that it is exceptionally important to include all collinear variables, as excluding any will cause worse coefficient estimates, strong ...

  3. Goldfeld–Quandt test - Wikipedia

    en.wikipedia.org/wiki/Goldfeld–Quandt_test

    Herbert Glejser, in his 1969 paper outlining the Glejser test, provides a small sampling experiment to test the power and sensitivity of the Goldfeld–Quandt test. His results show limited success for the Goldfeld–Quandt test except under cases of "pure heteroskedasticity"—where variance can be described as a function of only the underlying explanatory variable.

  4. Moderation (statistics) - Wikipedia

    en.wikipedia.org/wiki/Moderation_(statistics)

    This is the problem of multicollinearity in moderated regression. Multicollinearity tends to cause coefficients to be estimated with higher standard errors and hence greater uncertainty. Mean-centering (subtracting raw scores from the mean) may reduce multicollinearity, resulting in more interpretable regression coefficients.

  5. Variance decomposition of forecast errors - Wikipedia

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

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  6. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables ⁡ (+) = ⁡ + ⁡ + ⁡ (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...

  7. Ridge regression - Wikipedia

    en.wikipedia.org/wiki/Ridge_regression

    Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. [1] It has been used in many fields including econometrics, chemistry, and engineering. [2]

  8. TI-84 Plus series - Wikipedia

    en.wikipedia.org/wiki/TI-84_Plus_series

    The TI-84 Plus C Silver Edition was released in 2013 as the first Z80-based Texas Instruments graphing calculator with a color screen.It had a 320×240-pixel full-color screen, a modified version of the TI-84 Plus's 2.55MP operating system, a removable 1200 mAh rechargeable lithium-ion battery, and keystroke compatibility with existing math and programming tools. [6]

  9. Multiple comparisons problem - Wikipedia

    en.wikipedia.org/wiki/Multiple_comparisons_problem

    Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery". A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. [4]