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위키데이터

• ID : Q1419761

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1. Now, how can we tell if there is high collinearity among the three predictors?
2. Running the regress command with a binary outcome variable will not be problem because collinearity is a property of the predictors, not of the model.
3. The example in the documentation for PROC REG is correct but is somewhat terse regarding how to use the output to diagnose collinearity and how to determine which variables are collinear.
4. Collinearity (sometimes called multicollinearity) involves only the explanatory variables.
5. In practice, collinearity means that a set of variables are almost linearly combinations of each other.
6. The assumptions of ordinary least square (OLS) regression are not violated if there is collinearity among the independent variables.
7. There is an extreme situation, called multicollinearity, where collinearity exists between three or more variables even if no pair of variables has a particularly high correlation.
8. In this paper we introduce certain numbers, called collinearity indices, which are useful in detecting near collinearities in regression problems.
9. A well designed experiment minimizes the amount of collinearity between factors.
10. While selecting a different flight profile could mitigate the collinearity between slant range and altitude, the factors are mathematically related.
11. Therefore, we cannot completely eliminate the collinearity by adjusting the flight profile.
12. What we can do to break the collinearity is replace altitude with engagement angle, as shown in the bottom right of Figure 1.
13. Corollary 3.2 stated that collinearity is invariant under an isometry of a neutral plane; therefore, collinearity is invariant under an isometry of a Euclidean plane.
14. Further, in Exercise 3.25 of the Isometry section, you have shown that collinearity is not necessarily an invariant property for a transformation of a Euclidean plane.
15. Collinearity refers to two or more independent variables acting in concert to explain the variation in a dependent variable.
16. One assumption, often neglected in multivariable regression models, is collinearity.
17. Collinearity also inflates the standard errors of these estimates, causing inaccurate and inflated variances.
18. In this study, we aimed to evaluate the role of collinearity in previously published pediatric FOT reference articles which are often cited in most manuscripts.
19. We reviewed several publications in children since 2005, to estimate if collinearity had been taken into consideration before modeling and reporting the predictive regression equations.
20. In IBM's SPSS 19.0, Collinearity Diagnostic tests of the data revealed no independent variable with a condition index above 4.0; in addition, no two variables shared variance proportions above .50.
21. This hypothesis makes two predictions: (1) There will be a linear correspondence between the gene and the protein it determines (collinearity).
22. Predictor collinearity (also known as multicollinearity) can be problematic for your regression models.
23. In the context of a traditional linear regression model, what this means is that the uncertainty around the value of a regression coefficient increases with the level of collinearity.
24. As a result, an increase in collinearity between two predictors increases the standard errors associated with the coefficient estimates for those two predictors.
25. This increase in the standard errors caused by collinearity is known as bias.
26. Yet, correlated predictor variables—and potential collinearity effects—are a common concern in interpretation of regression estimates.
27. The authors demonstrate that collinearity cannot be viewed in isolation.
28. Rather, the potential deleterious effect of a given level of collinearity should be viewed in conjunction with other factors known to affect estimation accuracy.
29. Collinearity means that within the set of IVs, some of the IVs are (nearly) totally predicted by the other IVs.
30. Collinearity is spotted by finding 2 or more variables that have large proportions of variance (.50 or more) that correspond to large condition indices.
31. People like to conclude that collinearity is not a problem.
32. Collinearity, in statistics, correlation between predictor variables (or independent variables), such that they express a linear relationship in a regression model.
33. "Collinearity (statistics)" redirects here.
34. The term collinearity, or multicollinearity, refers to the condition in which two or more predictors are highly correlated with one another.
35. We touched on the issue with collinearity earlier.
36. In a regression context, collinearity can make it difficult to determine the effect of each predictor on the response, and can make it challenging to determine which variables to include in the model.
37. A mapping of a geometry to itself which sends lines to lines is called a collineation; it preserves the collinearity property.
38. In statistics, collinearity refers to a linear relationship between two explanatory variables.
39. Collinearity and unbalance can be visualized in a pairs plot (Figure 4-14), or measured by a correlation matrix in the case of continuous predictor variables.
40. To assess collinearity we only have to plot the x variables.
41. Unbalance or collinearity can be due to two different causes: First, subjects are nonrandomly missing in the data.
42. Collinearity (in a random sample) does not produce bias but it complicates the interpretation of the model coefficients.

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위키데이터

• ID : Q1419761

Spacy 패턴 목록

• [{'LEMMA': 'collinearity'}]
• [{'LEMMA': 'alignment'}]