Questions tagged [multicollinearity]

Situation when there is strong linear relationship among predictor variables, so that their correlation matrix becomes (almost) singular. This "ill condition" makes it hard to determine the unique role each of the predictors is playing: estimation problems arise and standard errors are increased. Bivariately very high correlated predictors are one example of multicollinearity.

789 questions
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Interactions: mean centering, standardizing and standardized coefficients (betas)

I mean-center my independent and moderator variable before calculating the interaction term to avoid multicollinearity. In my regression output table, I subsequently report the standardized ...
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Controlling for variables in social sciences

I know this is a completely hypothetical scenario but I just want to understand how the effect of a variable could be held constant and how the coefficients of two independent variables are estimated ...
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Multicollinearity in simple linear regression

If there's perfect or near multicollinearity problem in a simple linear regression $y_i = a + b x_i + u_i$, what characteristics does $x_i$ have? I think if there's perfect multicollinearity, it ...
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Keeping two correlated variables in the model

I am using OLS: In my model I have two variable (X1 & X2) which are correlated (correlation = 0.47). My prediction is that X1 should be negatively associated with Y and X2 should be positively ...
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Does collinearity of two features affect the predictive performance of support vector classifier?

I have a set of features for my machine learning model (support vector classifier, SVC), two of which are strongly positively correlated (i.e., diameter and spherical volume). Does this affect the ...
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Collinearity among OLS regressors may inflate their variance, but can it also change their estimated value?

I've read that collinearity between independent variables in an OLS regression may inflate the variance of the OLS estimates. But, can it also change their estimated value?
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Collinearity when regressing against three sets of dummies

I would like to regress price of food products against three sets of dummy variables: 1. the food product itself (13 products) 2. the country where the food product was priced (119 countries) 3. the ...
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How to improve on the preferred regression model?

After running the chow test, the F stat shows structural change is present in the model, so the unrestricted models are preferred. I am not sure how I can choose from the two regression models in the ...
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How to assess whether the couples are significantly more correlated with their partners than other study participants?

I am looking for statistic advise. I have a small sample (86 participants) of which 30 are husband and wife (15 couples) and the rest are either single or have a partner but the partner is not in the ...
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Interpreting dummy variable interaction terms

I am attempting to model monthly retail electricity sales. To account for both the effects of seasonality and weather, I created an interaction term by multiplying 12 monthly dummy variables by the ...
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Interactions terms and the dummy variables

I am attempting to model monthly retail electricity sales. To account for both the effects of seasonality and weather, I created an interaction term by multiplying 12 monthly dummy variables by the ...
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Is “$\mathrm E[X'X]$ has rank $k$” the assumption of no multicollinearity?

My lecturer wrote this on the board: Assume $\mathrm E[X'X]=Q$ has rank $k$, where $X$ is the data matrix and $k$ is the number of independent variables. I asked her if that is the assumption of ...
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How is GVIF calculated for categorical variables?Also is there any other way to detect multi co-linearity of categorical variables?

I was tring to find a way to remove the redundant categorical variables as features. I believe GVIF would give high value for the redundant/multicollinear categorical variables. Please let me know if ...
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Interpretation of removed continuous variables in regression due to linear dependence

I have created a standard OLS regression model to estimate the House Price and one group of variables describe the age group percentage of population in a particular neighborhood (ranging 0 to 100). ...
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Including or excluding a variable and what about the p-values when VIF = 5.2?

I'm writing my master thesis and run into a question about multicollinearity. I have two interaction effects which have a high VIF (5.2, 4.8). Both are interaction effects between categorical ...
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How to estimate the VIF for geeglm models in r

I am very new in r and at analyzing gee models. I have a very high dimensional data (51 independent variables measure at multiple times with no missing values (secondary dataset)). I am pretty sure ...
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OLS Multicolleniarity

I have a pretty simple task to estimate ols multiple regression. I need a measure of multicolleniarity. Is condition number a good measure and what criteria exists fot its value?
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Error in vif.default(glm.fit1) : there are aliased coefficients in the model

I have 12,000 records and I'd like to predict a two-class outcome. The dataset has 3 numeric predictors and twenty categoric predictors. The problem is that I have perfect collinearity somewhere ...
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How do I diagnose collinearity with rfe() from the caret package?

I have 12,000 records and I"d like to predict a two-class outcome. I'm deciding which predictors to keep and I'm having trouble with two problems. 1- I get an error message because I have categories ...