# Tagged Questions

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### Estimability and less than full rank model matrices

So I have some data... ...
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### Should I de-mean a predictor variable before a dummy interaction

Suppose I have the following time-series linear model where $\beta$ is misspecified: $Y(t+1) = \alpha + \beta X(t) + \sum_{i=1}^{10000}\gamma_i Z_i(T) + \varepsilon$ where all parameters are in ...
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### Intuition for consequences of multicollinearity

So we have a regression equation with one explained variable and 10 explanatory variables. What I have read so far: Multicollinearity doesnt affect the regression of the model as a whole. But if we ...
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### Multicollinearity and Splines :: Is there a problem?

When using natural (i.e. restricted) cubic splines, the basis functions created are highly collinear, and when used in a regression seem to produce very high VIF (variance inflation factor) ...
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### Variance partitioning - why be cautious?

I'm about to use variance partitioning to interpret my results of a given model and across models and have come across various criticisms of it most notably by Pedhazur (1982, 1997). Also, the ...
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### Multicollinearity when individual regressions are significant, but VIFs are low

I have 6 variables ($x_{1}...x_{6}$) that I am using to predict $y$. When performing my data analysis, I first tried a multiple linear regression. From this, only two variables were significant. ...
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### Assessing multicollinearity of factors

I have a multifactor model (with 7 factors currently) and 754018 observations. In order to check for multicollinearity issues as the model grows I wrote an R script to compute a correlation matrix ...
I am currently assessing multicollinearity in my datasets. What threshold values of VIF and condition index below/above suggest a problem? VIF: I have heard that VIF $\geq 10$ is a problem. After ...