collinear

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col·lin·e·ar

(kŏ-linē-ăr)
Acting along the same line.
[L. col- (con-), with, + linea, line]
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CIRCLES can reduce collinearity of the anchor points; all areas within the concentric circles can be localized.
This interaction term had consistent parameter estimates across models, so it was unlikely that collinearity caused parameter instability.
Indicators of collinearity are: parameter tests are insignificant for theoretically important parameters, parameter tests are insignificant whereas the whole model test is significant, large standard errors for regression coefficients, extreme variability in parameters across samples, large changes in parameters when changing data or either adding or removing other variables, unexpected signs for parameters, and decreases in regression standard errors when removing a variable.
Collinearity analyses of Brachypodium with wheat and barley are very limited because their genome sequences are not available yet.
and [w.sub.i] in (2) is regarded as a collinearity equation in image space, and the orthogonal projection of [m.sub.i] to [w.sub.i] can be written the n as
Regression assumptions were checked and met, including testing independent errors (Durbin-Watson statistics = 2.05), collinearity (tolerance values > .81), and linearity and homoscedasticity (P-P plots).
Regression Diagnostics: Identification Influential Data and Sources of Collinearity. New York: John Wiley & Sons, Inc.
Model B Error Beta 1 (Constant) -4.398 1.509 C1 13.124 1.001 .338 C2 9.017 1.078 .230 OF_TI_P .280 .031 .220 TE1_OF_P -.132 .005 -.666 Collinearity Statistics Model t Sig Tolerance VIF 1 (Constant) -2.915 .004 C1 13.107 .000 .672 1.487 C2 8.361 .000 .591 1.693 OF_TI_P 9.102 .000 .767 1.304 TE1_OF_P -28.733 .000 .833 1.200 (a.) Dependent Variable: GAP_TI_P TABLE 8--COLLINEARITY DIAGNOSTICS (a) Model Dimension Eigenvalue Condition Index 1 1 3.634 1.000 2 .687 2.301 3 .535 2.607 4 .093 6.240 5 .051 8.469 (a.) Dependent Variable: GAP_TI_P
However, "good instruments" are never easy to find, and instrumental variable methods with weak instruments may be more misleading than ordinary estimation with collinearity. However, in this instance there is clear precedence.