# 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.

992 questions
Filter by
Sorted by
Tagged with
8k views

### Assessing the Contribution of each Predictor in Linear Regression

Say I build a linear regression model to identify linear dependencies between variables in my data. Some of these variables are categorical variables. If I want to evaluate the contribution of a ...
24k views

### what is the difference between collinearity and interaction?

I cannot differentiate clearly between "interaction" and "collinearity" in multiple linear regression. For me these terms are related but not the same. I have searched the forum but could not find ...
272 views

### Principal component (PC) as a substitute for colinear covariates?

I am working on a spatial linear regression and I can tell there is collinearity between covariates. Can I use PCA (Principal Component Analysis) images instead of original covariates to estimate the ...
1k views

### Correlation and collinearity in regression

I did a correlation analysis for my variables. All of them are associated (the coefficient is above 0). However, there is no collinearity problem in my regression analysis. I do not know how to ...
749 views

### Multivariate analysis for incomplete fractional factorial design

We have conducted a survey experiment with varying amounts of incentive (factor 1 = I1, I2, I3, I4, I5). The experiment was conducted stepwise in three subsequent studies (factor 2 = S1, S2, S3). ...
4k views

### Highly correlated predictors in backward stepwise regression?

I know that it's not right to enter variables having multicollinearity (high correlation) into a regression analysis. But if I'm using backward stepwise regression could I add all the highly ...
3k views

### Mahalanobis distance in a hierarchical cluster analysis in SPSS

I am conducting a hierarchical cluster analysis in SPSS on my database with several neuropsychological and psychiatric variables. In my database, some of my variables (that is, two pairs of variables) ...
1k views

### Time dummies in panel data — absorbing effects?

I am conducting a data analysis. I have a panel with individual firms with firm-specific and macroeconomic variables. I would like to run an OLS regression adjusted for firm clustering effects and ...
107 views

### Multicollinearity in a “population model”

I was talking with a colleague who told me that at the time of making logistic regression across a population did not have to worry about assumptions such as multicollinearity, because when analyzing ...
1k views

### Pattern mining on a small data set

I have a small data set 30 features/predictors and 30 observations. My target variable is Oil production and my predictors are well & reservoir properties (depth, trajectory, temperature, pressure ...
58 views

### Logistic Regression - Two Dependent variables not playing well with each other

My name is Abhi and I trying to teach myself logistic regression by solving some of the problems available on the internet. I am using R and RStudio as the development environment Problem Statement ...
938 views

### Low correlation between predictor variables in linear regression

I know that one if one is trying to perform linear regression, multicollinearity can be an issue because it can "lead to unreliable and unstable estimates of regression coefficients." Suppose for a ...
13k views

### VIF in GLM model in R

Before running or building a model, ho can we check on the multicollinearity between different covariates in GLM model in R? I know that SAS Proc MIXED procedure gives a column for VIF which is very ...
65 views

### Correlated explanatory variables where both are significant

I am running a multiple linear regression using SPSS to test the effect of ethnicity and ethnic/racial attitudes or perceptions on political predispositions. One model, as an example, looks like ...
10k views

### Why does Ridge Regression work well in the presence of multicollinearity?

I am learning about ridge regression and know that ridge regression tends to work better in the presence of multicollinearity. I am wondering why this is true? Either an intuitive answer or a ...
60 views

### Evidence of collinearity, yet significant coefficients – is this a problem? [duplicate]

I have two main explanatory variables of interest in the model I use. It is only when I include them both that the coefficients are significant. The correlation between them is 0.98 and the vif-value ...
1k views

### Multicollinearity with Interaction (high VIF)

When I check the VIF of my independent variables with the dependent variable, it looks normal and less than 5 but when I add the interaction variables, the VIF increase to 48 for some variables. I ...
1k views

### Why is least squares performing as well as ridge regression when there is multicollinearity?

I am learning about ridge regression, so I am implementing it in MATLAB as practice. However, I am having trouble finding a structure of data where ridge regression performs better than an ordinary ...
450 views

### perfect collinearity among multiple continuous variables

When there is a perfect collinearity among more than two continuous variables, how do you deal with it and how are the regression results interpreted? I have three independent variables which ...
67 views

### Non-linear model robustness

I am building a non-linear model aiming to describe the mechanistic process of resource allocation. There several terms, and what makes the model non-linear is competition between lines that are ...
7k views

### VIF calculation in regression

I want to use VIF to check the multicollinearity between some ordinal variables and continuous variables. When I put one variable as dependent and the other as independent, the regression gives one ...
49k views

### When can we speak of collinearity

In linear models we need to check if a relationship exists among the explanatory variables. If they correlate too much then there is collinearity (i.e., the variables partly explain each other). I am ...
587 views

### Is multicollinearity a concern in nonparametric statistics?

Should I be concerned about multicollinearity in nonparametric statistics?
204 views

### Can I use simple regression with variables that are not significant in multiple regression?

As I am testing a number of models, I notice that none of my demographic variables are ever signficant. For example, I am testing a model to predict the dependent variable 'perceived substitutability'....
106 views

### multicollinearity in OLS regression [duplicate]

If I have a dependent variable Y and two independent variables X1 and X2 , that are highly correlated. Y ~ beta1*X1 + beta2*X2 What issues can multicollinearity cause in an OLS regression, apart ...
2k views

1k views

108k views

### How to test and avoid multicollinearity in mixed linear model?

I am currently running some mixed effect linear models. I am using the package "lme4" in R. My models take the form: ...