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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Determining Multicollinearity for the given ScatterPlot Matrix

Given this data: should I be concerned with multicollinearity here?
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Mathematical Basis behind inflation of Standard errors of Regression estimates due to multicollinearity

We know that due to multi-collinearity, the standard errors of beta estimates get inflated. But what is the mathematical basis to it? I am looking for some mathematical relationship or something to ...
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VIF around 3.5 in two covariates, how shall I deal the problem?

in my multinomial logistic regression model (sample size n=290) I am adjusting the results for a group of covariates (n=8). I tested them for multicollinearity and if most of them have a VIF lower ...
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How to tell the difference between linear and non-linear regression models?

I was reading the following link on non linear regression SAS Non Linear. My understanding from reading the first section "Nonlinear Regression vs. Linear Regression" was that the equation below is ...
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Genetic algorithm for parameter estimation in nonlinear model [closed]

I use Genetic Algorithm function in GA package for parameter estimation in nonlinear model. I use a simulation data, which all of the variables have multicolinearity problem. When I use GA function, I ...
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High correlation between two independent variables, but no multicollinearity?

I have two independent variables which have a Pearson correlation coefficient of 0.98. The two independent variables measure the same underlying construct but only at two different points in time ...
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Interactions and Multicollinearity

I have a question concerning multicollinearity in OLS. I built a very simple model: y = a + x(x) + y(x) + e. In this case x is a significant predictor, y is not. Both of them are time related ...
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How can I compute multicollinearity (VIF) in R, and know if it's safe?

I am working on a group project for a university course on "big data research methods". My data is aggregated by neighborhoods in Chicago. My dependent variable $Y$ is the property crime rate (per ...
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Multicollinearity in polynomial regression

How to deal with multicollinearity in polynomial regression? Suppose I have $x$, $x^2$ and $x^3$ as independent variables in my regression equation. How can I calculate and remove multicollinearity ...
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Combining Collinear Variables

I have a set of 10 variables: 9 explanatory, 1 response. I wish to do a constrained regression on the variables and use the values of the coefficients as weights in a TOPSIS analysis. I am having ...
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What is the difference between VIF and stepwise regression?

What is the difference between the variance inflation factor (VIF) and stepwise regression as both help in detecting multicollinearity? What variables are different while running both techniques?
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Automatically fixing ill-conditioning or collinearity

I'm backtesting a regression model, which entails running it on a bunch of bootstrap samples of a "rewound" version of our data set. Unfortunately, in some of these resamplings, I end up getting some ...
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Linear regression multicollinearity

I run linear regression with Posttest scores as DV and Pretest scores and Group as IVs. Collinearity Statistics Tolerance shows .998 both for Pretest and Group (VIF 1.002). Is this one of the ...
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How to Deal with Multicollinearity?

I use cross-sectional macroeconomics variables with OLS. I found that my data suffers from multicolinearity and i am looking for solutions. I read about first differences of the variables and i tried ...
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How to calculate vif of each term of model in R? [migrated]

I am beginner in R doing modelling in R, I loaded excel sheet in R, i have chosen x elements and y elements then fitted model for linear and second order regression. Now I have both models. I am bit ...
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Multicolinearity in logistic regression using R

Upon performing binary logistic regression, I have found VIF, using R programming, as follows: ...
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How do two variables that with a non-significant low correlation (<.10) become negatively associated in regression? [duplicate]

I have a correlation between variable A and B that is non-significant and small (r = .09). When examining a hierarchical regression with three control variables entered at step 1, and three variables ...
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Multicollinearity and differences between independent variables

I am trying to estimate a model of attrition that takes into account characteristics of an individual's current job and characteristics of an individual's residency. Of particular interest is how the ...
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Multicollinearity Multiple Choice Question

I have one exam question. Anyone can answer and explain the reason for me? You are studying the following $$ Y = \beta_0 +\beta_1X_1 +β_2X_2 +u. $$ You know that the two variables $X_1$ and ...
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Multicollinearity and two variable with the same level of significance

I have a high value of correlation between 2 of my explanatory variables (0.79), and they are both significant at the same level exactly. Besides that they are both important to the model. The ...
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Consequence of Multicollinearity

In case of perfect multicollinearity the predictor matrix is singular and therefore cannot be inverted . Under these circumstances, the ordinary least-squares estimator $\hat\beta=(\Bbb X'\Bbb ...
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VIFs and condition indexes give different answers about multicollinearity

For a multiple regression model, all the variables have p-values below 0.05. The p value for the whole model is below 0.05 as well. When I checked for multicollinearity, I got VIFs below 5 for all the ...
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How does partial least square deal with collinearity?

I have little knowledge about statistics and PLS. Can I answer the question as below? The PLS model is linear, but the coefficients in the model are found in a different way. The principle is that ...
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How to measure the concurvity? (MGCV)

In the R package MGCV there is a function to measure the level of concurvity https://stat.ethz.ch/R-manual/R-patched/library/mgcv/html/concurvity.html What is the formula behind that? Is there any ...
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Multicollinearity in binary logistic regression analysis after latent class analysis

I am attempting to conduct a binary logistic regression but I have come to a roadblock relating to multicollinearity. I originally conducted a latent class analysis which resulted in the ...
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Interpreting signs of coefficient estimates for collinear variables in glmer

I'm doing an glmer on data in which multiple participants attempt to identify multiple words. Word identification accuracy (ACC) is the dependent measure. I want to test whether two variables related ...
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Highly correlated variables in random forest

In my understanding, highly correlated variables won't cause multi-collinearity issues in random forest model (Please correct me if I'm wrong). However, on the other way, if I have too many variables ...
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How to prevent collinearity?

Ieno & Zuur 2015 describe a number of causes of collinearity among explanatory variables entered into a linear regression. One of these causes is what they call a 'data collection' cause. They ...
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Variance Inflation Factor and Condition Indeces

My data is cross-sectional macroeconomics data. I have six independent variables (x1,x2,x3,x4,x5,x6) plus 2 dummies (d1,d2) plus 2 interactions terms (d1*x1,d2*x1).I am testing my data for ...
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Multicollinearity Using VIF and Condition Indeces

I am testing my dataset for multicollinearity using VIF and condition indices(CI).My dataset is cross-sectional macroeconomics data. I have 6 independent variables (x1,x2,x3,x4,x5,x6) plus 2 dummies ...
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VIF & CI in Regression with Dummies and Interaction Terms

I am checking my dataset for multicollinearity using VIF and condition indices(CI).My dataset is cross-sectional macroeconomics data (n=75). I have six independent variables (x1,x2,x3,x4,x5,x6) plus ...
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VIF Values for Multicollinearity

I have cross-sectional data which I obtained from panel data. All the variables are on macroeconomics with n=75. I want to check my variables for multicollinearity using VIF. I got the following ...
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Multicollinearity: does if matter which variable I remove?

Say I'm running a multiple linear regression. I have 4 explanatory variables: A, B, C and D. Pearsons correlation coefficient for A and B is 0.90. I decide to remove either A or B prior to running ...
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Multicollinearity as interaction terms added: Separate or common analysis?

Using OLS, the starting aim of my analysis is to study how different types of credit card contracts affect the dependent variable (y: use of credit cards). I have generated three dummies (v1, v2, v3) ...
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Multiple linear regression: does BIC drop (vaguely) collinear variables?

Say I have the following multiple linear regression: Y ~ X1 + X2 + X3 + X4 All X variables are independent, but X1 and X2 look kind of linearly related when ...
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How to interpret VIF?

I am using the vif function in the R package car to test for multicollinearity. I am a little confused at the output given. For example, I have 5 variables (x1, x2, ...
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Does a decrease in standard error from one model to another suggest collinearity?

Say I have model 1 and model 2. y1 = b0 + b1x1 + u y2 = b0 + b1x1 + b2x2 + u If there is an increase in the standard error of x1 from model 1 to model 2, does this suggest collinearity between the ...
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How to interpret this?

Good day again! For my thesis, I used 2 types of test to measure 3 types of dimensions for Perefectionism. The results for the correlation is everything is correlated (p>0.01). Then when I computed ...
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Multicollinearity? Non-significant multiple-linear regression, highly correlated regressors but low variance inflation factor

I have a small sample (30 obs) and 4 independent variables, 3 of which are significantly correlated to each other. I have tried to run the simple linear regression on each of them separately and it ...
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Multicollinearity and interaction terms

My dataset has 2734 observations (but in some speficitations, that number reduces to 1280). I also have interaction terms (in some specifications, even fourth-order terms). As far as I know, ...
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How multicolinearity affect the prediction

In a linear regression model, if some of the predictors are correlated, then in the output of most software, you will see very large p-value in those coefficients and very high standard error. My ...
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Elastic net is being used in genome wide analysis. Similar approach would work for survey analysis?

I'm approaching the elastic net procedure for genome wide analysis (GWAS) because it allows for feature selection, groups detection and improved validity. It's a powerful technique when you have many ...
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How seriously should I consider the effects of multicollinearity in my regression model?

I have a model y ~ x + z and the correlation between x and z is 0.2. This is only weakly positive. So, how seriously should I consider the effects of ...
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Change in order of predictors breaks logistic model estimation (glm, R)

I am fitting a binomial logistic regression in R using glm. By chance, I have found out that if I change the order of my predictor variables, glm fails to estimate the model. The message I get is ...
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Technique for estimating sensitivity to multiple correlated variables

The problem is estimating linear coefficients (in this case, the output is neural firing rate, and the parameters represent sensitivity to different sensory inputs) to multiple potentially correlated ...
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VIF for generalized linear model

Is the variance inflation factor useful for GLM models. Below example shows OLS is showing VIF>5, but GLM lower. GLM shows instability in the coefficients between train and test set. ...
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Multicollnearity in backward selection approach

When I build my most parsimonious model using a backward selection approach, do I have to worry about multicollinearity. I mean, do I first check for multicollnearity and drop the variables which has ...
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Multiple regression - how to deal with mixed linear and non-linear variables

Say I have a bunch of explanatory variables to predict a continuous independent variable. Below, a simple toy example: I think it would be easiest to do a log-log transform and proceed with linear ...
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Linear regression model mistakenly gives $R^2$ equal to 1

I'm using R to create a linear regression model from survey data about public sentiment for a new technology. I am encountering a problem where the addition of a new explanatory variable raises the ...
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Co-linear covariate in regression what if take one out?

I understand that there are a few ways to deal with correlated or co-linear covariates ( PLS or AIC, and Lasso) But my problem here is: If you have 2 covariates x1 and x2 that are correlated(you ...