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Questions tagged [multicollinearity]

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How do I build a multiple Regression with four predictors that include interactions (moderators) and in which correlations shall be calculated?

I understand that * means that we check for the association between two predictor variables and + means that we add another predictor variable to the model we already have. But how do I write a function (lm) for the following question: Variable A…
user21012331
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Correlation/multicollinearity between multiple dependent variables

I am in doubt what the right approach is for analyzing multiple dependent variables that seem to be correlated. So far I found only regression type of answers; assuming one dependent variables and multiple independent variables. The problem here is…
Danique
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Is there a way to derive GVIF in Jamovi?

Apparently the car package vif function and performance package check_collinearity function both calculate generalized variance inflation factor (GVIF) automatically if a categorical variable is entered into a regression. As an example, this can be…
Shawn Hemelstrand
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How to perform autocorrelation, heteroscedasticity and multicollinearity tests using the package ‘systemfit’ in R?

I'm estimating a system of equations with Seemingly Unrelated Regression (SUR) of the ‘systemfit’ package, but in the documentation I have not found how to perform the autocorrelation, heteroscedasticity and multicollinearity tests (Variance…
Luisadieze
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I want to deal with multicollinearity, but I m confused which column to drop

How to deal with Nan columns? Which column to drop to avoid collinearity I tried to check the co relation between features, before going ahead which feature I should drop to avoid multicollinearity ?
Anjali Wadhwani
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Difference in difference model: interpretation of parallel trend assumption

I'm following this diff in diff study where I want to see the effect of student grants of enrollment. there are 12 provinces and 3 of them applied the policy of giving student grants from 2016 on. I have to test if the parallel trend assumption…
Fabio Bordignon
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Implications of regressing Y = f(x1, x2) where Y = x1 + x2 + x3

In various papers I seen regressions of the sort of Y = f(x1, x2), where f() is usually a simple OLS and, importantly, Y = x1 + x2 + x3. In other words, regressors are exactly a part of Y. These papers used regressors as a way to describe data…
user154682
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Panel data in R for thesis

I am currently working on my master degree thesis and it's about linear regression on panel data and statistical learning. I'm looking for someone who could help me with the multicollinearity and heteroskedasticity in panel data, and who to…
Miriam Chiarello
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collinearity of random forest regression

I am new to random forest regression. I am trying to build a random forest regression to predict company's earning. Do I need to worry about collinearity problem for random forest regression? Thanks! If there is a collinearity problem, how can I…
Yujing Sun
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MultiCollinearity when VIF is 0

What does it mean when VIF(Variance Inflation Factor) is 0? Does it indicate No MultiCollinearity?
Deepak Mittal
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Checking for multicollinearity and adjust the data

I would appreciate help interpreting the following pairwise scatterplots of predictor variables to check for multicollinearity and then fit the data to the results to avoid this occurring. Background: I am working on a task where I have to carry…
idlatva
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Multuicollinearity test in multinomial regression with categorical independent variables in R

I am running a multinomial regression with categorical independent variables. To check the multicollinearity, I used the VIF function from the car package, but it shows the following warning: In vif.default(model1) : No intercept: vifs may not be…
saroja
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time trends vs year fixed effects: is peaceful coexistence possible?

How can I deal with collinearity between a time variable and year fixed effects? I have a panel model where the outcome is measure in three separate years. I am using OLS from the fixest package. One of the variables is gasprice. It takes the same…
YouLocalRUser
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Individual P-values in Logistic Regression

I ran a logistic regression with like 10 variables (with R) and some of them have high P-values (>0.05). Should we follow the elimination techniques that we follow in multiple linear regression to remove insignificant variables? Or is the method…
datanovice
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How to interpret the variables when we drop an independent variable to avoid collinearity in Regression?

I went through this and this. So whenever there are dummy variables which happen to be collinear, we need to drop one of them to avoid collinearity. My confusion is how would I interpret the value of the other dummy variables with respect to the…
Kiera.K
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