# Tagged Questions

Multicollinearity means predictor variables are correlated with each other, making it harder to determine the role each of the correlated variables is playing. Mathematically, it means the standard errors are increased. Multicollinearity can have counter-intuitive effects.

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### Combine two regression models when variables are highly correlated

I investigated the relation between an angle $\alpha$ and a sensor value $x$. So I have $\alpha = f(x)$ which can be modeled here as a simple polynom. When I want to use this relationship in a real ...
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### Zero inflated GLM and singularities

So I am using a zero-inflated model to (1) model the presence/absence of an animal over certain habitat characteristics using a binomial distribution (2) model the count data over the same ...
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### Ridge Regression Plot by Direct Calculation [closed]

I would like to emphasize that ridge regression coefficients is becoming close to zero as the penalty parameter $\lambda$ increases, but without using R package (glmnet, lm.ridge). My procedures are: ...
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### Is dependency created when adding one variable which is the difference of two existing variables to a regression model?

I have two variables $x_1$ and $x_2$ in linear regression. I would like to see if the distance between $x_1-x_2$ is significant. So I want to add one more variable $x_3$, which is equal to $x_1-x_2$. ...
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### Too many variables and multicollinearity in OLS regression

After reading material related to my topic, I understood that multicollinearity among predictors would result in singular matrix $X'X$, and that leads to noninvertible matrix. Thus, the solution will ...