# collinearity

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## collinearity

[kō‚lin·ē′ar·əd·ē]
(cell and molecular biology)
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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(b) Movement trajectory: a well designed movement trajectory can eliminate collinearity (coplanarity) problem and make full use of the real-time information, that is, the distribution of unknown nodes, environment information, and so forth.
Diagnostic tests for collinearity are: VIF (variance inflation factor), correlation matrix of variables, eigenvalues and eigenvectors, condition indices, variance proportions.
It can be seen from (11) that the object-space collinearity error function can be minimized with respect to the rotation matrix A only.
Regression assumptions were checked and met, including testing independent errors (Durbin-Watson statistics = 2.15), collinearity (tolerance values > .82), and linearity and homoscedasticity (P-P plots).
Psychological Involvement -- * p < .05, ** p < .01 TABLE 2 Collinearity Statistics for Multiple Regression Equations Equation 1 Equation 2 DV=Psychological DV=Attitudes Involvement Ind.
Coefficients Unstandardized Collinearity Coefficients Statistics B Std.
In order to decrease the extent of descriptor interaction, several descriptors must be removed from the model while monitoring the condition index obtained from running a collinearity diagnosis.
The output shows no presence of collinearity. The mean absolute percentage error whose formula is indicated in the formula (2) was calculated in order to test this first regression model.
Error Beta t Value p Val- ue Age -.085 .040 -.142 -2.134 .034 Highest degree 4.717 .657 .657 7.178 .000 Class size .038 .069 .037 .547 .585 Membership in Professional or- ganizations .910 .427 .145 2.133 .034 Regression Coefficients Collinearity Statistics Tolerance VIF Age .979 1.021 Highest degree .911 1.098 Class size .965 1.036 Membership in Professional or- ganizations .932 1.073 Table 3 Multiple Regression Analysis Results: Teachers' Characteristics and Attitudes toward HBPI (n=159) Variance Explained R R Square Adjusted R Square Std.
Moderate collinearity is symptomatic of OLS regression of many variables, and is anticipated especially in the initial model, which simply represents a means to a more parsimonious solution.

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